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Journal of Economic Theory 159 (2015) 1016–1055
www.elsevier.com/locate/jet

On the smoothness of value functions and the existenceof optimal strategies in diffusion models
Bruno Strulovici a,∗,1 , Martin Szydlowski b
a Department of Economics, Northwestern University, 2001 Sheridan Road, Evanston, IL 60208, USAb Department of Finance, Carlson School of Management, University of Minnesota, 321 19th Ave South, Minneapolis,

MN 55455, USA
Received 3 October 2013; final version received 26 March 2015; accepted 31 March 2015
Available online 14 April 2015

Abstract
Studies of dynamic economic models often rely on each agent having a smooth value function and awell-defined optimal strategy. For time-homogeneous optimal control problems with a one-dimensional
diffusion, we prove that the corresponding value function must be twice continuously differentiable underLipschitz, growth, and non-vanishing-volatility conditions. Under similar conditions, the value function of
any optimal stopping problem is shown to be (once) continuously differentiable. We also provide sufficientconditions, based on comparative statics and differential methods, for the existence of an optimal control inthe sense of strong solutions. The results are applied to growth, experimentation, and dynamic contracting
settings.
© 2015 Elsevier Inc. All rights reserved.
JEL classification: C61; C73; D86; O40
Keywords: Optimal control; Optimal stopping; Smooth pasting; Super contact; Markov control; HJB equation* Corresponding author.

E-mail addresses: b-strulovici@northwestern.edu (B. Strulovici), szydl002@umn.edu (M. Szydlowski).
1 Strulovici gratefully acknowledges financial support from an NSF CAREER Award (Grant No. 1151410) anda fellowship form the Alfred P. Sloan Foundation (BR2013-006).
http://dx.doi.org/10.1016/j.jet.2015.03.015
0022-0531/© 2015 Elsevier Inc. All rights reserved.

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1. Introduction
Ever since Louis Bachelier formalized the concept of Brownian motion to study financial
markets, diffusion processes have played an increasingly important role in economic analysis. Diffusions are routinely used to model macroeconomic and financial shocks, news arrivalin learning and experimentation settings, stochastic output and demand, as well as many otheruncertain processes. Harnessing the relevant techniques has proved a challenge to economists,
however, so much so that it has motivated a book by Dixit (1993a) on the “Art of Smooth Pasting,” which included a heuristic justification for the differentiability of value functions at optimal
stopping thresholds.
In dynamic models, the smoothness of value functions is often necessary to investigate
the properties of optimal decisions and payoffs, as illustrated by the celebrated Benveniste–
Scheinkman theorem. In growth models, for instance, it is used together with the Bellman –or, in continuous-time, the Hamilton–Jacobi–Bellman “HJB” – equation to establish and showthat an agent’s optimal consumption and investment decisions are monotonic in her wealth andother parameters of the model. In dynamic contracting models, the agent’s optimal effort andconsumption, his continuation utility, and the impact of the principal’s risk attitude are typicallyanalyzed via the HJB equation, even though the equation typically has no closed-form solution.
In bandit problems, many properties of the optimal experimentation strategy can be similarly
characterized without an explicit solution, as we also illustrate.
In these and other applications, one needs to establish, before proceeding to any further analysis, that the value function is smooth enough to solve the HJB equation.2 For the optimal control
of diffusions, “smoothness” thus requires (and means in this paper) twice differentiability of thevalue function, and is sometimes called the super contact condition.3 In pure stopping problems,“smoothness” requires (and means) that the value function is once differentiable, and is known
as the smooth pasting property.
Unfortunately, economists do not have available at hand a simple theorem guaranteeing theappropriate level of smoothness for the value functions arising in their diffusion models. “Verification theorems” are used to check that a smooth candidate solution is equal to the value function,
but they neither provide this candidate solution, nor do they guarantee its existence. In optimal
stopping problems, the usual arguments for smooth pasting are heuristic, and they typically assume that the optimal stopping region takes on a very specific “threshold” form. In optimal
control problems, economists have had to rely on ad hoc arguments to establish that the valuefunctions of their particular models solved the HJB equation.
This paper provides two theorems for the smoothness of value functions, based on simple
and readily checked conditions. These theorems pertain to infinite-horizon models, ubiquitousin economics, in which the state is a one-dimensional, time-homogeneous diffusion. The first
theorem states – under continuity, linearity, and non-vanishing-volatility conditions – that thevalue function of any optimal control problem is twice continuously differentiable and solves
everywhere the HJB equation corresponding to that problem. The theorem equally applies to
2 Unless stated otherwise, solutions should be understood in a “classical” sense. To exploit the HJB equation and derive
properties of value functions and optimizers, classical solutions are significantly simpler and easier to work with thanother solution concepts.
3 The super contact condition has been extensively used to characterize optimal solutions. See, e.g., DeMarzo andSannikov (2006) and DeMarzo et al. (2012).

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bounded and unbounded state spaces, and requires only that the control lie in a compact set.
In particular, the control space need not be convex or one dimensional.
This theorem relaxes several assumptions, problematic in economic applications, made in
the large body of work dealing with the existence of solutions to boundary value problems,
which include HJB equations. For example, Krylov (1980, Theorem 5, p. 24) assumes a boundedstate space and a convex control space, which respectively rules out growth models and bandit
problems; Noussair (1979), Safonov (1989), and Gilbarg and Trudinger (2001) assume that thecoefficients of the diffusion are Hölder continuous in the state, a property that is obviously violated when the control space is discrete4 ; Fleming and Soner (1993, p. 161) assume a boundeddomain and bounded coefficients with continuous derivatives in the state.5
We focus on the existence of twice differentiable (or “classical”) solutions because manymethods to obtain qualitative properties of the value function (which in turn determine propertiesof the optimal action and the state trajectory) rely on twice differentiability.6 More general solution concepts, such as weak7 or viscosity8 solutions, do not guarantee the existence of a second
derivative. This makes it difficult to qualitatively characterize the value function and to use afirst-order condition to derive the optimal control in terms of its derivatives, since these may notexist.
Our second smoothness theorem states, under the same conditions, that the value function of
any stopping problem is continuously differentiable, as long as the terminal value function has
this property.9 For both theorems, the conditions were previously known to be necessary for the
results.10 We are not aware of previous theorems showing that these conditions, taken together,are sufficient.
Just because the value function of a control problem is smooth, this does not imply the existence of an optimal control for that problem – not even when the HJB equation has a maximizer
at every state. The issue is whether the candidate control coming from the HJB equation generates a well-defined trajectory for the state process. Establishing the existence of an optimal
control is of clear interest in many settings. For instance, it may be used to show the existence ofa best-response and, subsequently, of an equilibrium, in continuous-time games, or in arguments
using an optimal control to derive properties of the value function. Most obviously, existence isalso required to derive properties of the optimal control itself.
Our second contribution is to provide sufficient conditions for the existence of an optimal control in the problems described above. There are two concepts of solutions to stochastic differential
equations: The strong one requires that the state trajectory be entirely determined by the control
4 These results, unlike ours, apply to multidimensional optimization, which explains some of the more stringent conditions that they assume.
5 They relax the bounded-coefficients assumption on p. 163, at the cost of assuming a more specific functional form
for the drift coefficient. Their method applies to multidimensional control problems with time-dependent coefficients.
6 See, e.g., the concavity arguments in Sannikov (2008) and DeMarzo and Sannikov (2006) and the qualitative characterization obtained by Shreve et al. (1984).
7 See e.g. Krylov (1980), Chap. 4.
8 See Crandall and Lions (1983) and Lions (1983).
9 The arguments can be extended to combined control and stopping problems. See Section 5.
10 Touzi (2010) provides an example with an unbounded control space in which the value function is not differentiable.
Øksendal and Sulem (2007, p. 139) consider a pure stopping problem in which the terminal function is not differentiable,
and show that the value function is not differentiable either at one of the stopping thresholds. With a vanishing volatility,
the smoothing effect of the Brownian motion is absent at some states, and it is easy to build deterministic examples inwhich the value function is not twice differentiable.

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process and by the realization of exogenous uncertainty. This solution corresponds to one’s intuition based on discrete-time stochastic optimization. The weak solution concept requires onlythat there exist some probability space for which the state process and Brownian motion satisfy
the state equation. Here, the Brownian motion is determined as part of the solution, and the state
trajectory need not be determined by the realized path of the Brownian motion. While weaksolutions are easily shown to exist,11 they are of limited interest when it comes to economic applications. Indeed, while the vast majority of economic models specify the source of uncertainty
explicitly, ruling out the possibility that it might be freely chosen, the weak solution concept
supposes that the structure of uncertainty is endogenous to the solution. Moreover, even when
the control is a function of the current state, i.e. Markovian, the trajectory of a weak solution is
not pinned down by this function and the realized path of the Brownian motion used to model
exogenous uncertainty. This makes the discrete-time intuition and interpretation of a controlled
state inapplicable, in which the agent reacts to exogenous shocks and generates the state trajectory. Allowing weak solutions thus introduces conceptual differences between continuous-timeand discrete-time models which are not present under strong solutions.
In economic models with a controlled diffusion, there are currently few options, other thanfinding an explicit solution, for guaranteeing the strong existence of an optimal control.12 Thisdifficulty can be easily seen in any control problem where the control set is binary, such as a twoarmed bandit problem. In these problems, any nontrivial Markovian control has to “jump” as the
diffusion hits some regions of the state space, creating discontinuities in the coefficients generating the stochastic equation for the state. This destroys the Lipschitz continuity typically required
for the existence of a solution; in these applications, the coefficient is not even continuous.
Fortunately, there exist specific results for the case of one-dimensional diffusions, which guarantee the existence of a unique strong solution as long as the volatility coefficient has boundedvariation.13 Building on these results, we identify conditions for the existence of an optimal
control. These conditions are all based on the HJB equation of the control problem, exploiting
the fact that the value function indeed solves that equation. Proving the existence of an optimal
strategy thus provides a first application of our smoothness results.
Some of our existence results are based on establishing, via an envelope theorem, differentiability of the control, and are illustrated in several applications. Our other existence results are
based on the theory of monotone comparative statics: if one can show that the control constructedfrom the HJB equation is monotonic, or at least piecewise monotonic, this will guarantee that it
has bounded variation and, under a simple regularity condition, that the volatility has boundedvariation. Proving such monotonicity is nontrivial, because it is typically impossible to explicitly
compute the value function and its derivatives. However, it is precisely for this kind of situations
that the theory of comparative statics has been developed.
Our results are illustrated with several applications. The first one concerns an optimal growth
problem. Relaxing usual concavity and convexity assumptions, we show that the value function is always smooth and solves the Bellman equation under standard regularity conditions. We
11 Measurability and local integrability of the controlled drift and non-degeneracy and local integrability of the squared
inverse of the controlled volatility suffice to show such existence. See e.g. Karatzas and Shreve (1998), p. 339.
12 Stroock and Varadhan (1979), Chaps. 6 and 7 and Krylov (1980), Theorem 1, p. 87, developed results showing theexistence of weak solutions. For more general existence results with weak solutions, see the survey by Borkar (2005).
It is also well known, and shown in this paper (Section 3), that there always exist ε-optimal controls that generate strong
solutions.
13 The first such result is due to Nakao (1972). See Section 4.

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also discuss conditions for the existence of an optimal control. Our second application concerns
multi-armed bandit problems with a common state. Each arm is a choice that is informative about
the true state of the world, and has a state-dependent payoff. The value function is, without any
further assumptions, always twice differentiable. When the number of arms is finite, the number of switching points between arms is shown to be finite (and uniformly bounded), although
it may be strictly optimal to use a given arm over disjoint intervals of beliefs. We also provide
general conditions for the existence of an optimal control when the agent can also allocate resources across two arms, rather than merely choose one of them. The third application revisits theprincipal-agent model analyzed by Sannikov (2008), proving the existence of an optimal contract
under a simple condition on the effort cost function.14 We also provide, as a simple illustrationof our results, a general proof that the option value of waiting in optimal stopping problems is
positive and increasing in the volatility of the underlying process.
1.1. Literature review
In addition to the papers already mentioned, twice differentiability in optimal control problems has been studied by Evans (1983), who assumes a finite control space, and for specificmodels in Operations Research (see, e.g., Harrison and Taksar, 1983). A variety of methods havebeen used to establish that property. Krylov (1980), Chapter 4, relies on probabilistic methods
and Gilbarg and Trudinger (2001) and Safonov (1989) rely on fixed point arguments. By contrast, the present results are obtained via the shooting method and are based on the insights of
Schrader (1969). They can easily be extended, for example to include vanishing volatility asin the extension of Appendix D or to include boundary conditions involving the derivative, as in
Szydlowski (2014). Similar results have been obtained via less elementary methods by De Costerand Habets (2006), Theorem 1.3, p. 77, Hartman (1960), and, for the case of bounded domains,
Bebernes (1963). As in the present paper, De Coster and Habets exploit Nagumo’s condition toprove their smoothness result. They assume the existence of so-called lower and upper solutions
to the boundary value problem. Our method delivers upper and lower solutions as a byproductand we do not need to assume their existence a priori.
Recent results on the necessity of smooth pasting are provided by Dayanik and Karatzas(2003), Peškir and Shiryaev (2006), Chap. 9.1, and Villeneuve (2007), using methods based on
the scale function of the SDE, or by “excessivity” of the value function. These results focus on
settings without flow payoffs. The integral of the flow payoff may be converted into an additional
state. Unfortunately, this conversion creates a multidimensional state space, in which some states
have zero volatility. Peškir and Shiryaev (2006) propose a method for proving the necessity ofsmooth pasting which, they mention, can be extended to multidimensional states. However, thatmethod requires a minimal volatility condition that is not satisfied by the converted state. Pham(2009, Proposition 5.2.1) contains a result similar to ours based on viscosity solutions, but does
not spell out the conditions on the primitives under which the viscosity solution has the Lipschitz
property used in his analysis.
Fleming and Soner (1993), p. 159, prove the existence of a strong solution for controlled SDE
problems and assume a priori that the control is Lipschitz in the state (and jointly continuous
14 Without this condition, the continuation utility process derived for the contract characterized in that paper is only
guaranteed to exist in the sense of a weak solution.

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in the time-state space, when it also depends explicitly on time). Yong and Zhou (1999), Theorem 5.2, p. 68, prove a similar result for linear controlled SDEs assuming a convex action set.
The present paper generalizes both results.
The paper is organized as follows. Section 2 introduces the general optimal control problem.
Section 3 proves that the value function is twice continuously differentiable. Section 4 provides
sufficient conditions for the existence of an optimal control. Section 5 turns to the optimal stopping problem, proving the continuous differentiability of the value function. Sections 3–5 include
examples illustrating their respective results. Section 6 concludes. Technical proofs are in Appendices A–F.
2. Control problem
We are given a filtered probability space , F, {Ft }t∈R+ , P which satisfies the usual conditions and whose outcomes are identified with the paths of a standard Brownian motion, denotedby B.15
We consider a process {Xt }t∈R+ controlled by another process {At }t∈R+ , taking values in anonempty closed interval X of R with (possibly infinite) endpoints x < x̄, and following the¯
dynamic equation
dXt = μ (Xt , At ) dt + σ (Xt , At ) dBt
X0 = x.

(1)

Assumption 1. There exists a nonempty compact metric space K such that At ∈ K for all t .16
A control process is said to be admissible if it is adapted to the filtration {Ft }t∈R+ , satisfies
Assumption 1, and Eq. (1) has a unique strong solution.17 The set of admissible control processes
is denoted by A.
Given an admissible control A, the agent’s expected payoff is given by
⎡ κ
⎤



(2)
v (x, A) = E ⎣ e−rt f XtA , At dt + e−rκ g(XκA )⎦ ,0
where XtA is the process starting at x and controlled by A, f XtA , At is the flow payoff at time t ,/ (x, x̄)} is the first time at which the boundary of X is hit, and g(x) and g(x̄) areκ = inf{t : Xt ∈¯ payoff is well
given constants.18¯ Assumptions 2 and 3, stated shortly, guarantee that the expected
defined for any admissible control (see Lemma 1 below).
15 We refer the reader to Karatzas and Shreve (1998) for the standard concepts used in this paper.
16 The assumption that K is independent of x is only for expositional simplicity. The analysis can be extended to thecase in which the control set depends on x, provided that i) for each x, K(x) is a nonempty, closed subset of the compact
metric space K, and ii) the correspondence x → K(x) is continuous. See Footnote 29. Alternatively, the compactness
assumption on K can be dropped, for example to allow for an unbounded control space, provided that the functions f , μ,and σ are bounded in a and uniformly continuous in (x, a). See Appendix C.
17 This definition of admissibility is the one used, among others, by the classic control theory textbook of Fleming andSoner (1993). See also Pham’s (2009) recent textbook.
18 We follow the usual convention of setting e−rκ g(X A ) = 0 whenever κ = ∞.
κ

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The (optimal) value function19 of the problem starting at x, denoted v(x), is defined byv(x) = sup v(x, A).

(3)

A∈A

An admissible control is said to be optimal if v(x, A) = v(x).
Assumption 2. There exists K > 0 such that, for all (x, x  , a) ∈ X 2 × K,






μ (x, a) − μ x  , a + σ (x, a) − σ x  , a + f (x, a) − f x  , a ≤ K|x − x  |,and the functions μ(x, ·), σ (x, ·), f (x, ·) are continuous in a, for each x.20
The last assumption contains several bounds on the primitives: standard linear growth conditions, a uniform lower bound on σ , and a condition guaranteeing that, for any control, the state
grows at a rate slower than the discount rate.
μμμAssumption 3. There exist constants K1 , K2 , K σ , K f , and σ such that21 K2 < r and 0 < σ ≤¯¯μμ|σ (x, a)|, |μ(x, a)| ≤ K1 + K2 |x|, |σ (x, a)| ≤ K σ (1 + |x|), and |f (x, a)| ≤ K f (1 + |x|) forall (x, a) ∈ X × K.
3. Twice differentiability of the value function
Our objective is to prove that the value function is twice differentiable in the interior of X andsolves the Hamilton–Jacobi–Bellman (HJB) equation
1
0 = sup −rv(x) + f (x, a) + μ (x, a) v  (x) + σ 2 (x, a)v  (x)
2
a∈K

(4)

with given boundary conditionsv(x) = g(x) if x is finite, and v(x̄) = g(x̄) if x̄ is finite.
¯
¯
¯

(5)

Theorem 1. Under Assumptions 1–3, the following holds:i) The value function is finite and has linear growth: |v(x)| ≤ Kv (1 + |x|) for some positive
constant Kv .
ii) The HJB equation has a twice continuously differentiable solution which has linear growth.
iii) Any solution to the HJB equation that has linear growth is equal to the value function. In particular, the value function is twice continuously differentiable and is the unique solution withlinear growth of the HJB equation.
19 To avoid confusion, we reserve the expression “value function” to the optimal expected payoff, and use the expression“expected payoff” when the control is arbitrary.
20 When K is a finite set, the continuity assumption is vacuous.
21 The condition K μ < r can be dropped if f is bounded. In that case, the control problem is equivalent to one in2
which X is replaced by a smooth increasing transformation Y of X, that satisfies this condition, without affecting otherconditions.

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Before going into the proof of Theorem 1, let us comment briefly on the necessity of ourassumptions. First, if σ is not bounded away from zero, the value function need not be differentiable, and the HJB equation need not have a classical solution.22 Moreover, existence anduniqueness of solutions for the controlled SDE (weak or strong) may not be guaranteed. Second, if either μ or σ violate the linear growth condition, the state Xt may explode in finite time.
Similarly, if the payoff function f violates the linear growth condition, the value function may
be infinite even if the state grows at a slower rate than the discount rate.23 To apply the techniques of Section 4 where we prove the existence of a strong solution, the Lipschitz assumption
on σ cannot be easily relaxed (see Footnote 43). The Lipschitz conditions on f , μ, and σ arealso used to guarantee uniqueness and continuity of solutions to the ODE problems that arise in
several proofs of this paper.
Finiteness is only an issue when X is unbounded, and follows from the following lemma,
proved in Appendix A.
μLemma 1. For any admissible control A, initial condition x, and r̃ > K2 , we have:i) limt→+∞ E|XtA |e−r̃t = 0, and∞ii) E[ 0 e−r̃t f (XtA , At ) dt] < +∞.
μThis lemma, combined with the linear growth condition on f and the fact that K2 < r immediately implies that v is finite and satisfies a linear growth condition.24
The rest of the proof consists of the following steps: 1) Prove the existence of a solution, w, tothe HJB equation, which has linear growth; 2) Construct a control process based on this solution;
3) Prove that the solution is equal to the value function of the problem and that either the control
constructed in 2) is optimal, or that it can be approximated by a sequence of admissible controls.
These steps will imply that any solution of the HJB equation with linear growth must coincide with the value function of the problem and, therefore, will show the uniqueness claimed inPart iii) of Theorem 1.
We first show the existence of a solution to the HJB equation. This result follows from Proposition 1 below, which is proved in Appendix B. The proposition relies on the following conditions
for an arbitrary real-valued function H̄ (x, p, q) defined on X × R2 .
Condition 1. On any compact interval X0 of X , there exist constants M and K such that25 forall (x, p, q) ∈ X × R2
22 It is, for example, easy to construct such cases when σ is identically equal to zero, at least for some control value.
Many studies of viscosity solutions deal with dynamic equations for deterministic problems that fail to have a classicalsolution. If σ vanishes only at some point (such as x = 0) and otherwise satisfies an ellipticity condition, particulartechniques may be used to extend the result. One such example is contained in Appendix D.
23 For example, suppose that {X }t t≥0 is a geometric Brownian motion with growth rate μ < r and volatility σ > 0 andthat the payoff function is given by f (x) = x 2 (no control). Then, the value function is infinite if μ + σ 2 /2 > r, as iseasily checked.
μ24 Precisely, it is shown in Lemma 1 that E|X A | is bounded above by (|x| + 1 + K )eK2 t , uniformly in A. This,Xt
μand the linear growth condition on f guarantee that f (Xt , At ) grows at most at rate K2 < r, uniformly in A. Thisimplies that the integral payoff is bounded linearly in x, uniformly in A. The terminal payoff is bounded above by
max{g(x)1x>−∞ , g(x̄)1x̄<+∞ }, which is also finite.
25 Parts¯ ii)¯ and iii) imply that H̄ is jointly continuous in (x, p, q). Although we do not use this fact explicitly in the
proof, it provides a more direct explanation for why the second derivative of the solution is continuous.

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B. Strulovici, M. Szydlowski / Journal of Economic Theory 159 (2015) 1016–1055i) |H̄ (x, p, q)| ≤ M(1 + |p| + |q|),ii) |H̄ (x, p, q) − H̄ (x, p̃, q̃)| ≤ K(|p − p̃| + |q − q̃|),iii) H̄ is continuous in x for each (p, q).
Condition 2. For all (x, q), H̄ (x, ·, q) is nonincreasing in p.
Condition 3. For each K̄ > 0, there exist K1 , K2 > K̄ such that for all x ∈ X , and ε ∈ {−1, 1},
H̄ (x, K1 + K2 |x|, εK2 ) < 0 and H̄ (x, −K1 − K2 |x|), εK2 ) > 0.
Proposition 1. Suppose that H̄ satisfies Conditions 1–3. Then, for any finite υ and ῡ, there exists¯
a twice continuously differentiable solution to the equationw  + H̄ x, w, w  = 0
which satisfies w(x) = υ if x is finite, and w(x̄) = ῡ if x̄ is finite. Moreover, there exists a positive
constant Kv such ¯that ¯ ¯|w(x)| ≤ Kv (1 + |x|) for all x ∈ X .
We apply Proposition 1 to Eq. (4) by checking Conditions 1–3.
Proposition 2. Under Assumptions 1–3, the HJB equation (4) has a twice continuously differentiable solution w on X , which has linear growth.
Proof. Eq. (4) can be rewritten as
w  + H (x, w, w  ) = 0,

(6)where26
H (x, p, q) = max

2a∈K σ 2 (x, a)(−rp + f (x, a) + μ(x, a)q).

(7)

We show that H satisfies Conditions 1, 2, and 3. Leth(a, x, p, q) =2(−rp + f (x, a) + μ(x, a)q),
σ 2 (x, a)so that H (x, p, q) = maxa∈K h(a, x, p, q). Assumption 2 and the strictly positive uniform lower, p , and σμ(x,a)
bound on σ (Assumption 3) guarantee that σf2(x,a)
2 (x,a) are Lipschitz in x on(x,a) σ 2 (x,a)
compact intervals of X , uniformly in a. This guarantees that h is continuous in a and Lipschitz
in x on any compact interval X0 of X , uniformly in a. Moreover, because r and σ 2 are positive,
h is decreasing in p and Condition 2 is satisfied.
26 Because all functions are continuous in a, K is compact, and σ is bounded below away from zero, the supremum isachieved as a maximum.

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To verify Condition 1, we use the following inequality27|H (x, p, q) − H (x, p̃, q̃)| ≤ max

2a∈K σ 2 (x, a)|(−rp + f (x, a) + μ(x, a)q)

− (−r p̃ + f (x, a) + μ(x, a)q̃)| .
This implies that

2 
μ
μ
r|p − p̃| + (K1 + K2 |x|)|q − q̃| ,2σ¯
and proves the Lipschitz condition for any compact interval X0 of X . Similarly, the growth
condition follows because μ and f are bounded on any compact support and σ 2 is bounded
below by σ 2 > 0.
¯ of H in x, the last part of Condition 1, is the key to guarantee that the value function
Continuity
is twice differentiable, even when the optimal control jumps. It is due to Berge’s Maximum
Theorem. Because the objective h is continuous in a and Lipschitz in x, uniformly in a, it iseasy to show that h is jointly continuous in (x, a).28 Since also the action set K is compact, theMaximum Theorem applies, proving that H is continuous in x.29
There remains to verify that Condition 3 holds. H (x, K1 + K2 |x|, εK2 ) is negative whenever|H (x, p, q) − H (x, p̃, q̃)| ≤μμ−r(K1 + K2 |x|) + K f (1 + |x|) + (K1 + K2 |x|)K2 < 0.
μSince K2 < r, this inequality holds for all x ∈ R (and hence, on X ) if and only if
K2 ≥

Kf
μ
r − K2

μ

and

K1 >

K f + K1 K2
r(8)

Thus the assumptions in Proposition 1 are satisfied, which shows existence of a solution to the
HJB equation for arbitrary boundary conditions at x and x̄, whenever these points are finite. 2¯
What role does optimization play for smoothness?
As mentioned above, continuity of H in x is used to guarantee that the value function is twice
differentiable.30 This continuity comes from an application of Berge’s Maximum Theorem tothe objective function h. If, instead of a maximum selection x → â(x) of h, we had chosen an
arbitrary selection x → ã(x), the resulting function H̃ (x, p, q) = h(ã(x), x, p, q) would generally not be continuous in x. This explains why the expected payoff is twice differentiable forthe optimal control, whereas it may fail to be so for a strictly suboptimal control: recall that the
27 More generally, if H (θ) = maxa∈K h(a, θ), one can prove that |H (θ) − H (θ̃)| ≤ maxa∈K |h(a, θ) − h(a, θ̃)|. For
example, suppose that a, ã maximize h at θ and θ̃ , respectively. Then, H (θ) − H (θ̃) = h(a, θ) − h(ã, θ̃) ≤ h(a, θ) −h(a, θ̃) ≤ maxa∈K |h(a, θ) − h(a, θ̃)|. The other inequality is proved similarly.
28 This is shown as follows. We fix some values for p and q and omit them from the notation. Suppose that (a , x )n nconverges to (a, x). We have |h(x, a) − h(xn , an )| ≤ |h(x, a) − h(x, an )| + |h(x, an ) − h(xn , an )|. The first term converges to zero by continuity of h with respect to a, while the second term converges to zero because h is Lipschitz in x,
uniformly in a.
29 Berge’s Theorem also applies if the control domain K(x) depends on x and satisfies the conditions provided in Footnote 16.
30 This condition is sufficient but not necessary: what matters is that the curve x → (x, v(x), v  (x)) avoids any discontinuity of H .

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second derivative is given by v  (x) = −H (x, v(x), v  (x)). If H is discontinuous in x, the valuefunction cannot be twice continuously differentiable, or even twice differentiable there, since theleft and right second derivatives at x will be different.
Proof that the candidate solution w is equal to the value function v
We split up the proof into two inequalities.
Lemma 2. Let w be a solution to the HJB equation (4) that has linear growth, and let v(x, A)
be the expected payoff given any admissible control A. Then, w(x) ≥ v(x, A) for all x ∈ X .
Proof. The proof follows a standard verification argument, which is included here for completeness. For any fixed admissible control A and finite time T > 0, Itô’s formula implies, for the
diffusion X A controlled by A and starting at x, thate−r(T ∧κY ) w(XTA∧κT )T∧κT= w(x) +1e−rt −rw(XtA ) + μ(XtA , At )w  (XtA ) + σ 2 (XtA , At )w  (XtA ) dt
2

0
T∧κn

e−rt σ (XtA , At )w  (XtA )dBt ,

+

(9)

0

where κT is the first time that XtA leaves X ∩ [x − T , x + T ] and T ∧ κT = min{T , κT }. We have
κ = limT →∞ κT . The term e−rt σ (XtA , At ) is square integrable over [0, T ∧κT ] (see Appendix A)
and w  (x) is continuous and therefore bounded on [x − T , x + T ]. Therefore, the stochastic
integral has zero mean. Taking expectations and using (4), we get the inequality⎡ T ∧κ
⎤ TE⎣e−rt f (XtA , At )dt ⎦ ≤ w(x) − E e−r(T ∧κT ) w(XTA∧κT ) .
(10)0

The linear growth condition satisfied by w, along with Lemma 1, guarantees thatlimT →∞ E[e−rT w(XTA )] = 0. Taking the limit of (10) as T goes to infinity31 and using theequality w(XκA ) = g(XκA ) yields v(x, A) ≤ w(x). 2
For the reverse inequality, we first obtain a candidate optimal control A∗t from the solution to
the HJB equation w. This candidate need not be admissible, because the stochastic differential
equation (1) may fail to have a unique strong solution. We will use a result by Nakao (1972),
who has shown32 that a one-dimensional SDE has a unique strong solution if its drift is measurable and its volatility has bounded variation and is bounded away from zero.33 We exploit
this property to construct an approximation to the candidate control which is admissible and gets
arbitrarily close to the desired inequality.
31 The expectation of the integral converges, since the integrand goes to zero at a geometric rate, by Lemma 1.
32 More precisely, Nakao established pathwise uniqueness of weak solutions. This, combined with a major result due
to Yamada and Watanabe (1971), shows the existence of a (unique) strong solution. See also Veretennikov (1981) andRevuz and Yor (2001, p. 392) for related results.
33 Drift and volatility must also be bounded, which holds here for any compact interval of X .

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Lemma 3. Let w be a solution to the HJB equation (4) that has linear growth. Then w(x) ≤ v(x)for all x ∈ X .
Proof. We construct a candidate optimal control as follows. Take a solution to the HJB equation w, and define M(x) ⊂ K as the set of maximizers of the equation
1
rw(x) = max f (x, a) + μ(x, a)w  (x) + σ (x, a)2 w  (x).
2
a∈K

(11)

The objective is continuous in a and in x, and K is nonempty and compact. The measurable
maximum theorem,34 thus guarantees the existence of a measurable selection â (x) ∈ M(x) of
maximizers.
If the control A∗t = â(Xt ) is admissible, applying the previous verification argument, this timewith an equality, shows that w(x) = v(x, A∗ ) and, hence, w(x) ≤ v(x). In general, the volatility
function σ̂ (x) = σ (x, â(x)) can jump, violating the standard Lipschitz (or Hölder) continuity
conditions usually assumed for the existence of a strong solution and, hence, for admissibility of
the control.35 We circumvent this issue by the following approximation argument.
Fix any ε > 0 and consider a grid of X with equally spaced intervals of length η, to be chosen
shortly. We define the Markovian control ã by ã(x) = â(χ(x)) where χ(x) is the element of thegrid closest to x.36 By construction, ã is piecewise constant. By Nakao (1972), the SDE
dXt = μ(Xt , ã(Xt ))dt + σ (Xt , ã(Xt ))dBt(12)

has a unique strong solution, because the function x → σ (x, ã(x)) has bounded variation. Letting {X̃t }t∈R+ denote this solution, the control {Ãt }t∈R+ defined by Ãt = ã(X̃t ) is admissible. If
X is compact, the function f (x, a) + μ(x, a)w (x) + 12 σ (x, a)2 w  (x) − rw(x) is uniformly continuous in (x, a) on X × K. Therefore, we can choose a grid mesh η small enough to guaranteethat1
f (x, ã(x)) + μ(x, ã(x))w  (x) + σ (x, ã(x))2 w  (x) − rw(x)2≥ f (χ(x), ã(x)) + μ(χ(x), ã(x))w  (χ(x))
1+ σ (χ(x), ã(x))2 w  (χ(x)) − rw(χ(x)) − ε ≥ −ε.
2(13)Plugging this inequality in the verification argument based on (9), yields v(x, Ã) ≥ w(x) − ε/r.
Since v(x) ≥ v(x, Ã), taking the limit as ε goes to zero yields the desired inequality v(x) ≥ w(x).
We now show the result if X is unbounded, focusing on the case in which X = R (the casein which X is a half-line is treated similarly). To each Xn = [−n, n] corresponds a modulus
of continuity ηn such that (13) holds on Xn if the grid has mesh ηn and ã is constructed as
before. We construct ã on X1 by using the grid with mesh η1 on that domain, then extend it on
X2 \ X1 by using the grid with mesh η2 on that domain, and so on. This construction defines ãover R. Moreover, ã is piecewise constant with finitely many jumps over any Xn , and hence hasbounded variation over any compact interval of R. Finally, (13) holds, by construction, on theentire domain R. The rest of the argument is unchanged. 2
34 See Aliprantis and Border (2006), p. 570.
35 See, e.g., Karatzas and Shreve (1998), Chap. 5.2, Theorem 2.5.
36 We can adopt any arbitrary convention when there are two points of the grid that are closest to x.

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3.1. Example: optimal growth
The analysis of growth models typically relies on some smoothness of the value function, as
the success of the Benveniste–Scheinkman Theorem (1979) illustrates. When time is continuous,
smoothness takes on a particularly important role: it guarantees that the Bellman equation issatisfied everywhere, and, hence, that it can be directly used to derive properties of optimal
policies.37 In models without uncertainty, concavity assumptions are often required to establish
differentiability (see Benveniste and Scheinkman, 1979 and, more recently, Rincón-Zapatero
and Santos, 2009, 2010). Theorem 1 shows that such assumptions can be dispensed with when
uncertainty is modeled by Brownian noise.
Consider an agent with initial capital x who seeks to maximize his lifetime utility by managing
his capital through consumption, savings, and effort. Formally, the agent solves⎡∞⎤
sup E ⎣ e−rs u(XsA , As )ds ⎦A∈A0

subject todXtA = μ(XtA , At )dt + σ (XtA , At )dBt ,where the control domain K is a compact subset of Rk for some k ≥ 1, and the drift and volatility
functions μ and σ are such that X is always nonnegative and that 0 is an absorption point, withu(0, a) = μ(0, a) = σ (0, a) = 0 for all a. The dimensions of A can represent consumption,
leisure and effort, technological choices, etc. XtA is the capital available to the agent at time t .
We do not place any other restriction on the agent’s technology and utility functions apartfrom the Lipschitz and linear growth conditions required by Theorem 1. By assuming that, foreach ε > 0, σ (x, a) is uniformly bounded away from zero on [ε, +∞) × K, we can apply38
Theorem 1 to show that the value function is C 2 on (ε, +∞) for each ε > 0 and, hence, on theinterior (0, +∞) of the entire domain. Moreover, it solves for all x > 0 the HJB equation
1
rv(x) = max u(x, a) + μ(x, a)v  (x) + σ 2 (x, a)v  (x).
2
a∈K

(14)

In Appendix D, we allow the state space X to stretch all the way to 0, and the volatility
function σ to vanish at zero, and show how our arguments extend to that case.
A major issue in such environment is to establish the existence of an optimal strategy forthe agent. Section 4 provides general results which may be used in this setting. If the control A
consists only of a consumption choice, then the volatility σ is typically independent of A: theagent’s consumption affects the dynamics of the capital X only by reducing its drift. In that case,
Corollary 1 of Section 4 implies that any measurable maximizing selection a(x) of (14) defines
an admissible, optimal control. Section 4.1 analyzes existence for a more general control process.
37 One illustration is provided by Quah and Strulovici (2013), who show monotonicity of the capital growth in the
discount factor. Their analysis connects supermodularity properties of the objective function in the HJB equation.
38 The termination value g(ε) in (2) is set to the actual value function v(ε), so that the value function on [ε, +∞)coincides, on that domain, with the value function of the initial control problem over the entire domain [0, +∞).

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3.2. Example: multi-armed bandit
Consider a multi-armed bandit problem with a common unknown parameter θ̃ ∈ {θL , θH },representing a binary, payoff relevant state of the economy. The agent must choose at each time
an arm i in some compact set K, given his belief Xt ∈ [0, 1] = P r(θ̃ = θH |Ft ) about θ̃ . Given
the choice At of an arm at time t , the agent learns about θ̃ according to some equation39
dXtA = μ(XtA , At )dt + σ (XtA , At )dBt .
Because the belief is a martingale, μ is identically equal to zero. Moreover, from standard
computations (see, e.g., Bolton and Harris, 1999), the volatility has the form σ (Xt , At ) =Xt (1 − Xt )ξ(At ) and is thus entirely characterized by the vector {ξ(i)}i∈K . ξ(i) is the signal-tonoise ratio of the aggregate signal generated by arm i. The expected flow payoff from playingarm i at time t if the current belief is x is
f (XtA , At ) = E[π(θ̃, i)|XtA = x, At = i],where π is the expected flow payoff (or utility) if the state of the economy is θ̃ and the agent has
pulled arm i. This flow is necessarily linear in x : f (x, i) = xπ(θH , i) + (1 − x)π(θL , i).
The assumptions of Theorem 1 are satisfied over any domain Xε = (ε, 1 −ε) with ε ∈ (0, 1/2).
Therefore, Theorem 1 implies that v is twice differentiable and solves the HJB equation over any
such domain and, therefore, over (0, 1).
This shows that the value function of any Brownian multi-armed bandit problem in which the
payoff distributions of all arms depend on the same unknown parameter is twice continuously
differentiable and solves the HJB equation. This result implies, as shown in Appendix E, thatwith finitely many arms, the number of switching points between arms is finite (and bounded
above by a function that depends only on the number of arms), and that the optimal control iswell defined. It is also used to construct an example with four arms in which a given arm is usedon disjoint subsets of the belief domain.
With infinitely many arms, or if the agent can allocate divisible resources across arms, it isa priori unclear whether there exists a well defined optimal control to this problem. Section 4.2
provides a positive answer for a resource allocation problem with two arms.
4. Existence of an optimal control
The control constructed in the proof of Lemma 3 need not be admissible because it may
fail to generate a strong solution to the SDE (1).40 In the context of Examples 3.1 and 3.2, thetrajectories of the optimal capital stock or the experimenter’s belief may have no solution or have
39 Unlike a setting in which arms are independent, such as the one analyzed by Gittins (1979), here all arms relate to
the same state. Analyzing control problems with multidimensional states is beyond the scope of this paper.
40 Standard results, e.g. Karatzas and Shreve (1998), Theorem 5.15, guarantee that the constructed control generates a
weak solution of SDE (1). As argued earlier, we think that strong solutions are a more natural concept in most economic
applications and provide a natural continuous-time counterpart of discrete time models. With a strong solution, there
exists a measurable map : (C(R+ , R), B) → (C(R+ , R), B), where B is the Borel algebra on R, such that the state
trajectory is given by X(ω) = (B(w)) for a.e. ω (see, e.g., Cherny, 2002). Such map does not exist for weak-only
solutions: knowledge of the Brownian path and of the agent’s strategy does not suffice to determine the state trajectory.
Instead, the filtration must be enlarged to account for unmodeled uncertainty, in a way that is endogenous and may dependon the parameters of the problem.

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a weak solution which cannot be pinned down given the history of the Brownian motion. Thissection provides conditions for the existence of a maximizing selection that yields an admissible
optimal control. All the results of this section are based on the following theorem, which provides
a novel and straightforward application of Nakao (1972)’s main result.
Theorem 2. Suppose that the selection â of maximizers is such that the function σ̂ : x →
σ (x, â(x)) has locally bounded variation. Then, the control A∗t that it generates (as definedby Lemma 3), is admissible and optimal.
Proof. Admissibility follows from Nakao (1972), who has shown that a one-dimensional SDE
has a unique strong solution if its drift is measurable and its volatility has bounded variation andis bounded away from zero (see Footnote 32 for more details on this result). Optimality follows
from a standard verification argument. 2
The bounded variation condition is necessary for the result: Barlow (1982) provides a classof stochastic differential equations for which the volatility does not have bounded variation andthere does not exist a strong solution, even if the volatility is bounded below, away from zero.41
An easy case in which σ̂ has bounded variation is if σ (x, a) is independent of a, a case thatarises in many economic applications, such as the consumption choice problem of Section 3.1.
Corollary 1. Suppose that (x, a) → σ (x, a) is independent of a. Then, the control A∗t generated
by the selection â of maximizers (as given by Lemma 3) is admissible and optimal.
Proof. The function x → σ (x) is Lipschitz in x, by Assumption 3, and has therefore boundedvariation. The result then follows from Theorem 2. 2
Beyond Corollary 1, the volatility σ may depend on a subset, α, of the control. In that case,one way of guaranteeing that σ̂ has bounded variation is to check that the correspondence M
of maximizers has a selection â = (α̂, β̂) ∈ K1 × K2 ⊂ Rk1 × Rk2 , such that α̂ has boundedvariation,42 and to assume that σ (x, α) is uniformly Lipschitz continuous over X0 × K1 forany compact interval X0 of X . This implies that σ̂ has bounded variation, as guaranteed by thefollowing result (see Ambrosio and Dal Maso, 1990).
Proposition 3. Suppose that σ is uniformly Lipschitz43 continuous on X0 × K1 for any compact
interval X0 of X , and that there exists a selection â(·) = (α̂(·), β̂(·)) of M(·) such that α̂ hasbounded variation. Then, σ̂ has bounded variation.
Guaranteeing that the correspondence M has a selection with bounded variation would apriori seem a benign and easily satisfied requirement. However, we are not aware of any general
41 We are grateful to Nicolai Krylov for pointing this reference out to us.
42 See Ambrosio and Dal Maso (1990) for the definition of bounded variation for functions defined and taking valuesin Euclidean spaces. Continuity of the control is neither necessary nor sufficient, as it does not imply bounded variation.
43 The assumption that σ is Lipschitz cannot be easily relaxed: Josephy (1981, Theorem 4) has shown that for thecomposition f ◦ g to have bounded variations for all functions g with bounded variation, f must be Lipschitz.

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result of this kind.44 Similarly, it seems difficult in general to show the existence of an absolutely
continuous selection.
Fortunately, there are more specific strategies to guarantee that the control has bounded variation. If the state space X can be broken down into finitely many intervals over each of which
there is a selection of maximizers such that each component of α is either monotonic or differentiable, this will guarantee the existence of a selection with bounded variation over the entire
domain.45
Theorem 3. Suppose that σ is uniformly Lipschitz continuous on X0 × K1 , for any compact interval X0 of X , and that X can be decomposed into disjoint intervals {Xj }nj=1 over each of which

there is a selection â j = (α̂ j , β̂ j ) of M such that each component of α̂ j is either monotonic or
differentiable with locally bounded derivative.46 Then, there exists an optimal control, which is
Markov and characterized by the selector â defined by â(x) = â j (x) for x ∈ Xj .
jk1
Proof. Fixing j , let {χi }m
i=0 denote a partition of some compact interval I of Xj and {α̂k }k=1jdenote the first k1 components of â . We have


k1
m−1
m−1



j
j
|σ̂ (χi+1 ) − σ̂ (χi )| ≤ K|α̂k (χi+1 ) − α̂k (χi )| ,(15)
|χi+1 − χi | +
i=0

i=0

k=1
j

where K is the Lipschitz constant of σ over I × K1 . Each α̂k is monotonic or differentiable withlocally bounded derivative and, hence, has bounded variation. This and (15) show that σ̂ hasbounded variation over each Xj and, hence, over X . The result then follows from Theorem 2. 2
Sections 4.1 and 4.3 provide applications of Theorem 3 in which α is differentiable. Unfortunately, differentiability is violated in many settings, most obviously when the control set K
is discrete. Monotonicity offers an alternative way of exploiting Theorem 3. In many economic
problems, the optimal control is monotonic in the state: consumption is increasing in wealth,investment decreases with risk, etc. Establishing such monotonicity without knowing the objective function explicitly is precisely the focus of the theory of monotone comparative statics, as
described below.
In what follows, we focus on the case in which K1 is an interval of R (i.e., k1 = 1). A function
ρ(x, a) is supermodular on some domain X0 × K1 if for all ã ≥ a in K1 and x̃ ≥ x in X0 ,
ρ(x, a) + ρ(x̃, ã) ≥ ρ(x̃, a) + ρ(x, ã), and submodular if the reverse inequality holds on thatdomain (or, equivalently, if −ρ is supermodular). When ρ is differentiable in a, supermodularity
is equivalent to ρa being nondecreasing in x. When ρ is twice differentiable, supermodularity
44 Chistyakov (2004) defines a concept of bounded variation for correspondences, for which he proves selection theorems guaranteeing a selection of bounded variation. However, we are not aware of any work connecting Chistyakov’s
concept with the correspondence of maximizers in optimization problems. We are grateful to Vyacheslav Chistyakov for
his insights into this problem.
45 Another, weaker requirement, is to show the existence of a selection that is K-lower or K-upper Lipschitz. This
property implies that the selection can only jump in a single direction and implies bounded variation. It can be establishedby applying the monotone comparative statics techniques described in this section to a suitable change of variable. See
Amir and De Castro (2013) and the references therein.
46 This condition means that the derivative is bounded on any compact interval, and holds if the relevant components ofα̂ j are continuously differentiable.

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is equivalent to the cross partial being everywhere nonnegative. Supermodularity is a sufficientcondition for the maximizer correspondence x → M(x) to be nondecreasing in the strong set
order, which means that for all x ≤ x̃, a ∈ M(x), and ã ∈ M(x̃), we have min{a, ã} ∈ M(x)and max{a, ã} ∈ M(x̃). In particular, the selections constructed from the smallest and largest
maximizers, respectively, are nondecreasing for any supermodular function, and nonincreasing
for any submodular one.47
The following result focuses for simplicity on the case in which α = a, so that K1 = K ⊂ R.
Corollary 2. Suppose that σ is uniformly Lipschitz continuous on X0 × K, for any compact
interval X0 of X , and let
ρ(x, a) =1
σ 2 (x, a)−rv(x) + f (x, a) + μ(x, a)v  (x) ,(16)

where v is the value function of the problem, and suppose that X can be decomposed into nconsecutive intervals {Xj }j =1,···,n such that on each product Xj × K, ρ is either supermodularor submodular. Then, there exists an optimal control, which is Markov and characterized by aselector â of M such that â has bounded variation.
Proof. From (7), maximizing ρ with respect to a is equivalent to solving the maximization
problem in the HJB equation. Let â(x) = max M(x). Whether ρ is supermodular or submodular
on Xj × K, â is monotonic on this interval. The result follows from Theorem 3. 2
Remark 1. For any strictly positive function ζ (x, a), note that the HJB equation
1
0 = max −rv(x) + f (x, a) + μ(x, a)v  (x) + σ 2 (x, a)v  (x)2a∈K
has exactly the same maximizers as the equation
1 2

0 = max ζ (x, a) −rv(x) + f (x, a) + μ(x, a)v (x) + σ (x, a)v (x) .
2a∈KSuch transformations are useful for proving monotonicity or differentiability of a selection of
maximizers.48
In many economic problems (such as experimentation problems, or when the agent has an increasing concave utility flow), it is possible to show that the value function v has a constant sign,
is monotonic, and is either convex or concave.49 This yields the following application of Corollary 2. Let l(x, a) = 1/σ 2 (x, a), f¯ = lf , and μ̄ = lμ. Those functions are all primitives of thecontrol problem.
47 This result is easy to check. See Topkis (1978) or Milgrom and Shannon (1994) for a proof. The strong set order isalso called the Veinott set order (see Veinott, 1989).
48 The transformation ζ (x, a) = 2/σ 2 (x, a) removes v  (x) from the maximization problem, and was used for (7) and
Corollary 2. Another example, when μ is known to be strictly positive, is to use ζ (x, a) = 1/μ(x, a). This permits totake v  out of the maximization problem.
49 There are many techniques to establish this, either by analyzing the HJB equation, or by constructing various controls
to directly show that the value function must be increasing and convex. Applications in this paper provide several such
examples.

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Corollary 3. Suppose that i) σ is uniformly Lipschitz continuous on X0 × K, for any compact
interval X0 of X , ii) v is nonnegative, nondecreasing, and convex, and iii) μ̄ is nondecreasing ina and supermodular, f¯ is supermodular, l nonincreasing in a and submodular. Then, there exists
an optimal control, and this control is Markov and nondecreasing in x.
The corollary is straightforward to prove: its conditions guarantee that each term in (16) issupermodular.
The supermodularity assumed in Corollary 2 can be weakened in several ways. Indeed, it suffices that ρ satisfies the single-crossing property or Interval Dominance Order (IDO) propertyin (a, x) for asserting the existence of a monotonic selection on any given interval.50 Here is a
useful way of checking these properties: when ρ is differentiable with respect to a, the IDO property is guaranteed to hold51 over K × I, where I is any interval of X , if there exists a positive,
nondecreasing function γI (·) of a such that, for each x  > x  , ρa (x  , a) ≥ γI (a)ρa (x  , a). If γI
is constant over K, the inequality implies that ρ satisfies the single crossing property in (a, x).
If γI is identically equal to 1, we recover the supermodularity condition.
4.1. Example: optimal growth with consumption and portfolio optimization
We reconsider the example of Section 3.1. The agent chooses, in addition to consumption, thefraction ϕt ∈ [0, 1] of his wealth going to a risky asset, so that At = (Ct , ϕt ).52 The volatility
σ (Xt , At ) is now increasing in ϕt , and we cannot apply Corollary 1 anymore to prove the existence of an optimal control. However, if one can show that the agent’s risk-taking behavior isnondecreasing in his capital holdings, this will imply monotonicity of ϕ, and guarantee the existence of an optimal control, by Theorem 3. To guarantee nonnegativity of the capital process X,
we assume that the agent cannot consume when x = 0. This does not affect Theorem 1, as longas the feasible consumption set [0, c̄(x)] is continuous in x.53
Slightly simplifying the setting, the HJB equation for this problem is


1 2


rv(x) =
maxu(c) + [μ(x, ϕ) − c]v (x) + σ (x, ϕ)v (x) .
2(c,ϕ)∈[0,c̄(x)]×[0,1]Thus, the optimal ϕ maximizes the objective1
μ(x, ϕ)v  (x) + σ 2 (x, ϕ)v  (x).
2
If this objective function satisfies the single crossing property in (ϕ, x), there must exist a nondecreasing maximizing selection ϕ. If σ is Lipschitz continuous in ϕ, Theorem 3 then implies
that there exists an optimal control.
50 The single-crossing property (Milgrom and Shannon, 1994), generalizes supermodularity (Topkis, 1978), and is itself
generalized by the IDO property (Quah and Strulovici, 2009). Comparative statics also obtain for objective functions thatare quasi-concave and ordered by the location of their peaks, as studied by Karlin and Rubin (1956) and Lehmann (1988).
This latter ordering is also generalized by the IDO property.
51 See Quah and Strulovici (2009, Proposition 2).
52 We are ruling out short sales and borrowing, which guarantees that ϕ lies in the compact space [0, 1].
t
53 See Footnotes 16 and 29. We can, for example, take c̄(x) to be any continuous function that rapidly increases fromc̄(0) = 0 to some positive constant c̄. For the concavity argument below (Footnote 58), we also require that c̄(·) be
concave.

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The single crossing property holds if i) v is increasing and concave,54 ii) μ and σ are increasing in ϕ, and iii) for any ϕ1 < ϕ2−v  (x)σ 2 (x)v  (x)μ(x)(17)

is decreasing in x, where σ 2 (x) = σ 2 (x, ϕ2 ) − σ 2 (x, ϕ1 ) and μ(x) = μ(x, ϕ2 ) − μ(x, ϕ1 ).
Single crossing is then a direct consequence of Proposition 1 in Quah and Strulovici (2012).55
In turn, (17) holds if −v  (x)/v  (x) and σ 2 (x)/μ(x) are both decreasing. The first conditionmeans that the agent has decreasing absolute risk aversion in x, while the second condition isimmediate to check for any given drift and volatility functions μ and σ .56 Thus, we recover theintuition, stated above, that if the agent’s risk-aversion is decreasing in his capital, the control ϕ
is monotonic and there exists an optimal control.
The previous result relied on the value function exhibiting decreasing absolute risk aversion,
which is an endogenous property. The next approach avoids this problem. It proves differentiability of ϕ, exploiting another possibility offered by Theorem 3.
We specialize the model to a version of Merton’s consumption and investment problem.57
The safe asset has a constant return μ0 , while the a risky asset has a payoff that follows a geometric Brownian motion with a higher return μ > μ0 . We impose the condition μ < r, whichμis standard in the portfolio optimization literature and is equivalent to the inequality K2 < rof Assumption 3. The agent’s capital follows the equation
dXt = [Xt ((μ − μ0 )ϕt + μ0 ) − Ct ] dt + Xt ϕt σ dBt(18)

The agent has a twice differentiable flow utility u(·) such that u (c) > 0 and u (c) < 0 for allc ≥ 0. The HJB equation is
rv(x) =

1u(c) + (x (μ − μ0 ) ϕ + xμ0 − c)v  (x) + x 2 σ 2 ϕ 2 v  (x).
2(c,ϕ)∈[0,c̄(x)]×[0,1]max

It is easily shown that v  (x) > 0. The optimal consumption c(x) therefore satisfies the first-orderconditionu (c) = v  (x),or equals the corner value c̄(x). It is also easily shown that the value function is concave.58 There
54 Such properties may be shown from direct arguments, as in Footnote 58.

55 Conditions i) and ii) guarantee that the functions v  (x)σ 2 (x) and v  (x)μ(x) each have a constant sign and, thus,
are single crossing functions. Condition iii) is the signed-ratio monotonicity condition in Quah and Strulovici (2012),
which guarantees that the sum is also a single crossing function.
56 That condition can be interpreted as a sort of increasing marginal Sharpe ratio: the additional exposure to risk frommoving from ϕ1 to ϕ2 , has a better Sharpe ratio for higher capital levels than lower ones.
57 See Merton (1969, 1971) and Duffie (2001) for a general presentation.
58 Concavity is established as follows: for 0 < x 1 < x 2 and λ ∈ (0, 1), let x = λx 1 + (1 − λ)x 2 . Consider any admissible controls ϕ 1 , C 1 and ϕ 2 , C 2 chosen starting from x 1 and x 2 , respectively. Let ϕt = (λXt1 ϕt1 + (1 − λ)Xt2 ϕt2 )/(λXt1 + (1 − λ)Xt2 ) ∈ [0, 1] and Ct = λCt1 + (1 − λ)Ct2 . It is easy to check that the admissible control (ϕ, C) startingfrom x yields Xt = λXt1 + (1 − λ)Xt2 ≥ 0 for all t . Moreover, u(Ct ) ≥ λu(Ct1 ) + (1 − λ)u(Ct2 ), by concavity of u.
Discounting and integrating proves the claim.

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is a unique maximizer ϕ(x) to the HJB equation, given by59v  (x) μ − μ0
ϕ(x) = min − 
,1 .
xv (x) σ 2

1035

(19)

Proposition 4. The maximizing selection (c(x), ϕ(x)) of the HJB equation generates an admissible, optimal control.
Proof. It suffices to show that ϕ has bounded variation on Iε = {x ∈ (ε, 1/ε) : ϕ(x) ∈ (0, 1)},
which consists of disjoint open intervals, for each ε < 1. Following Theorem 3, it suffices to showthat ϕ is continuously differentiable on that domain. Rewriting the HJB equation, we have60




2

u(c)
+
(x(μ
−
μ
v  (x) = −
max
)ϕ
+
xμ
−
c)v
(x)
−
rv(x)
.
0
0
(c,ϕ)∈[0,c̄(x)]×[0,1] x 2 σ 2 ϕ 2
Since the maximizers c(x) and ϕ(x) are unique, Corollary 4, Part iii) of Milgrom and Segal(2002) implies that v  is differentiable at all x > 0. Moreover, the derivative is continuous, as iseasily checked. Therefore, (19) implies that ϕ is continuously differentiable on Iε . 2
4.2. Example: multi-armed bandit and resource allocation
We now reconsider the bandit problem of Section 3.2 with two arms, but in which the agent
has a fixed resource, normalized to 1, to allocate between the arms at each time.61 As before,
each arm has a payoff distribution that depends on the common parameter θ̃ and the state at time
t is the agent’s belief Xt = P r(θ̃ = θH |Ft ). The control of the agent is now a fraction At ∈ [0, 1]
allocated to the first arm. The total payoff from both arms in each state is π(θ, At ). Notice thateach allocation a ∈ [0, 1] yields a combination of two signals which may be aggregated into asingle signal with volatility σ (x, a) = ξ(a)x(1 −x) about the parameter θ̃ , and yields an expected
payofff (x, a) = xπ(θH , a) + (1 − x)π(θL , a).

(20)

Thus, we are exactly in the setting of Section 3.2, with K = [0, 1]. In particular, the value functionof the problem is twice continuously differentiable and solves everywhere on (0, 1) the HJB
equation
10 = max f (x, a) + (x(1 − x))2 ξ 2 (a)v  (x) − rv(x).
a∈[0,1]
2This equation may be rewritten as2
1v  (x) =
max
(x,
a)
−
rv(x)).
(f(x(1 − x))2 a∈[0,1] ξ 2 (a)

(21)

(22)

Therefore, the existence of a monotone optimal selection will be guaranteed if the function
59 If v  (x) = 0, the first term of the minimization is assumed to be equal to +∞.
60 The optimal allocation ϕ(x) is strictly positive for all x, since v  (x) > 0.

61 Examples of experimentation with resource allocations include (Bergemann and Välimäki, 1997, 2000; Bolton andHarris, 1999), and (Keller et al., 2005). In the last setting, one arm is safe, whereas both arms are “risky” in the present
model.

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ξ 2 (a)
ξ (a)

(23)

is either supermodular or submodular in (a, x).
Assuming that f (a) = f (1, a) − f (0, a) ≥ 0 for all a (i.e., θH is the “good” state of theeconomy), v is nondecreasing in x. Therefore, the first term in (23) is supermodular (submodular) if and only if ξ(a) is increasing (decreasing) in a. Similarly, the second term is supermodular(submodular) in (x, a) if f (a)/ξ 2 (a) is increasing (decreasing) in a. Combining these observations with Theorem 3 proves the following result.
Proposition 5. Suppose that ξ(a) and f (a)/ξ 2 (a) are either both increasing or both decreasing in a. Then there exists a monotone optimal selection a(x) and, therefore, an optimal control.
4.3. Existence of an optimal contract in principal agent problems
This application revisits the analysis contained in Sannikov (2008). The objective here isto provide conditions under which the optimal contract characterized in that paper generates astrong solution for the continuation value process of the agent. The existence problem discussed
here is actually relevant for many recent continuous-time principal-agent models. In the recursive approach to principal-agent models, the principal is viewed as “controlling” the continuation
value of the agent by providing him with consumption, and rewarding or punishing him depending on his output. If the continuation value process admits only a weak solution, the principal
may implicitly be randomizing in the contract, as the continuation value is not necessarily determined by the history of output alone. This raises several issues regarding the interpretation of thecontinuation value process as well as the principal’s risk aversion, as we argue below.
To save space, we only sketch the presentation and arguments already contained in Sannikov(2008), and refer the reader to that paper for a detailed exposition.
The principal chooses a consumption process {Ct }t≥0 and can implement, by choosing theright incentives, some effort process {At }t≥0 . Precisely, assuming that the agent’s continuation
utility Wt is adapted to the filtration generated by the exogenous uncertainty, the Martingale
Representation Theorem implies that
dWt = (rWt − u(Ct ) + h(At ))dt + ψ(At )dBt

(24)

where At is the level of effort that the principal chooses to implement, u and h are the utility andeffort cost functions of the agent, and ψ(a) = h (a) is the contract sensitivity to observed output.
The principal seeks to maximize the payoff⎡ τ⎤F0 = E ⎣ e−rt (At − Ct )dt ⎦0

subject to (24) and some initial participation constraint W0 = w.62
Therefore, the principal faces an optimal control problem in which the state is Wt and thecontrols are Ct and At , following the standard notation in principal-agent models.63 Sannikov
62 The stopping time τ is time the agent is either retired or fired.
63 Thus, what has been called “x” in earlier section is now “w”, whereas “A” now refers to only one component of thebidimensional control. F , instead of v denotes the value function of the problem.

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1037

has shown that the agent is “retired” when Wt hits some upper bound w̄ and fired when Wt
hits zero. The optimal consumption level c(w) is easily shown to be bounded by some level c̄
on [0, w̄], while the effort domain is assumed to be bounded above by some level ā.
The principal’s HJB equation on (0, w̄) is


1

2 
rF (w) =max(25)a − c + F (w)(rw − u(c) + h(a)) + ψ(a) F (w) .
2(a,c)∈[0,ā]×[0,c̄]
Under standard assumptions on u and h, Sannikov shows that the HJB equation has a solution
F that is twice continuously differentiable. Moreover, F is strictly concave.
At the optimal contract, standard results imply that there exists a weak solution to theSDE (24), whenever ψ(a(w)) is bounded away from zero.64 With a weak solution, the continuation value process is not necessarily adapted to the filtration generated by the Brownian
motion Bt , which implies that there is more randomness in Wt , and therefore the contract, than
generated by the output process.
One possible interpretation for having (only) a weak solution is that the principal is randomizing over continuation utility levels. However, the principal’s objective F (w) is strictly concave,which implies that he is risk-averse along the optimal contract. This makes this interpretation
problematic as a justification for a weak solution. The uncertainty arising from the broader filtration implied by weak solutions seems conceptually different from the one obtained if theprincipal explicitly allows all contractual variables to depend on other sources of uncertainty,
such as a second Brownian motion.
Another issue is that, as observed by Sannikov (2008, Footnote 29), the application of theMartingale Representation Theorem, which is a key step in the analysis, must also be modified
to include the larger filtration. In that case, the resulting representation of W cannot a priori
be reduced to a stochastic integral with respect to the initial Brownian motion. As a result, theanalysis of the contract, which was largely based on choosing the integrand of that stochastic
integral, must be modified. Intuitively, it raises the issue of what “sensitivity” the principal should
apply to the new sources of uncertainty. Guaranteeing a strong solution gets rid of these issues.
Proposition 6, below, provides simple conditions on the effort cost function h(a) under which
the continuation value process has a unique strong solution. Thus, Wt is adapted to FtB , and thecontract does not involve randomization.
Proposition 6. Suppose that the effort cost function h is increasing, three times differentiable,
and satisfies h(0) = 0, h (0) > 0, h (a) > 0 and h (a) ≥ 0 for all a ∈ (0, ā]. Then, the SDE
dWt = (rWt − u(Ct ) + h(At ))dt + ψ(At )dBt
has a unique strong solution.
The assumptions on h are satisfied, for example, if h(a) = γ1 a p + γ0 a for p ≥ 2 and
γ0 , γ1 > 0, as in Sannikov’s (2008) numerical example, if it is exponential, or is equal to any
positive combination of such functions.
Proof. Because consumption appears only in the drift of (24), it suffices to show that a(w) has
bounded variation. The optimality equation for a can be reduced to the optimization problem
1
max a + F  (w)h(a) + F  (w) ψ(a)2 .
(26)
a∈[0,ā]2
64 See, e.g., Revuz and Yor (2001, Corollary 1.12, p. 372).

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We will first show that the objective function in (26) is strictly quasiconcave in a, for each w.
That is, its derivative with respect to a, (a, w) = 1 + F  (w)h (a) + F  (w)ψ  (a)ψ(a), can cross
0 at most once as a increases, from above. We have, recalling that ψ(a) = h (a),a (a, w) = F  (w)h (a) + F  (w)(h (a)h (a) + h (a)2 ).
Our assumptions imply that h (a) > 0 for all a. We can rewrite the previous equation asa (a, w) =(a, w) − 1 h (a) + F  (w)h (a)h (a).
h (a)

Whenever (a, w) ≤ 0, the previous equation implies thata (a, w) ≤ −h (a)+ F  (w)h (a)h (a).
h (a)

Since h (a) > 0 and h (a) ≥ 0 for a > 0, and F is concave, we conclude that a (a, w) < 0
whenever (a, w) ≤ 0 and a > 0, proving strict quasiconcavity of the objective function in (26).
This also shows uniqueness of the optimizer a(w). Moreover, a(w) is continuous in w,
from (26) and Berge’s maximum theorem, whose conditions are easily checked here. Therefore, it suffices to show that a(w) has bounded variation on any interval (w1 , w2 ) over whicha(w) ∈ (0, ā). By Theorem 3, we will have proved the result if we show that a(w) is continuously differentiable on any such interval.
The optimal effort is determined on (w1 , w2 ) by the first-order condition (a(w), w) = 0. To
show continuous differentiability of a(w), we first observe that the optimal consumption c(w)is also unique, as an immediate consequence of (25) and the fact that u (c) is strictly decreasing
in c. An envelope theorem of Milgrom and Segal (2002, Corollary 4, Part iii)) then implies that F
is three times continuously differentiable and, hence, that  is differentiable with respect to w.65
Because a (a, w) is strictly negative, evaluated at a(w), the implicit function theorem can beapplied to the first-order condition, showing that a is continuously differentiable in w on thedesired domain. 2
5. Optimal stopping and smooth pasting
Optimal stopping problems appear in many economic settings such as labor search, experimentation with bandits, the exercise of real options, and the optimal choice of capital structurewith endogenous bankruptcy. The smooth pasting property, which states that the value function
must be continuously differentiable everywhere, can yield conditions which uniquely determine
the optimal stopping region. Typically, researchers apply guess and verify, that is, they assume
that the smooth pasting condition holds, use it to find the optimal stopping region, and verify that
this solution equals the optimal value function.66
This section establishes, under conditions similar to those of Section 2, that the value functionof any optimal stopping problem is continuously differentiable and provides a verification result.
65 The envelope theorem is applied to the modified HJB equation F  (w) = − max {2/ψ(a)2 [a − c + F  (w)(rw −a,cu(c) + h(a)) − rF (w)]}. Our assumptions on h guarantee that ψ(a) = h (a) is bounded below by h (0) > 0 and, hence,that the previous equation holds on (w1 , w2 ).
66 This is done for example by Leland (1994) in the context of optimal bankruptcy, Bolton and Harris (1999) andMoscarini and Smith (2001) for experimentation problems, McDonald and Siegel (1986) for real options, and Jovanovic(1979) for labor search.

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In particular, the value function is differentiable at any threshold at which stopping becomes
optimal, which is the smooth pasting property.67 For clarity, we separate optimal control andoptimal stopping problems. The problems can be combined with an appropriate extension ofTheorems 1 and 4.68 As will be clear from the arguments below, there is a strong relation between
the proofs of Theorems 1 and 4.69
Consider the optimal stopping problem⎡ τ⎤
v(x) = sup E ⎣ e−rt f (Xt )dt + e−rτ g(Xτ )⎦ ,
(27)
τ ∈T

0

where T is the set of all stopping times adapted to the initial filtration F and valued in [0, ∞],and {Xt }t∈R+ solves the equation
dXt = μ(Xt )dt + σ (Xt )dBt
subject to the initial condition X0 = x. We maintain the same assumptions as before on μ, σ
and f , which guarantee that the SDE has a unique strong solution, and, along with Assumption 4,that the expected payoff is well defined for all stopping times.
Assumption 4. g is C 1 and |g(x)| ≤ K g (1 + |x|) for some constant K g ≥ 0.
Theorem 4. Under Assumptions 2–4, the following holds70 :i) The value function is finite and has linear growth: |v(x)| ≤ Kv (1 + |x|) for some positive
constant Kv .
ii) v is continuously differentiable on the interior of X and satisfies v(x) ≥ g(x) for all x ∈ X .
iii) Let Y denote the subset of X for which v(x) = g(x). Then Y is closed and v solves thefollowing equation on X \ Y:w  (x) + 1

122 σ (x)(−rw(x) + f (x) + μ(x)w  (x)) = 0.

Proof. The proof of i) is similar to i) of Theorem 1. The subset of Y consists of all the states
at which it is optimal to stop immediately. By continuity71 of v and g, X \ Y consists of disjoint open intervals {Zi }i∈I . Pick any two points x1 < x2 in such an interval. The result statedin Appendix B guarantees the existence of a C 2 solution w to the ordinary differential equation
67 Given our continuity and linear growth assumptions on f (x) and g(x), we can show that the optimal stopping timeequals the first hitting time of the set {x ∈ X : v(x) = g(x)}. See Peškir and Shiryaev (2006), Corollary 2.9, p. 46.
68 In the combined problem, the optimal continuation region consists of disjoint open intervals, as in this section, and on
any such interval, the analysis of Section 2 can be applied, showing that value function of the controlled process is twice
differentiable. The analysis of the present section can then be replicated to show that the value function is everywhere C 1 ,replacing the dynamic equation (28) by the HJB equation, which is satisfied on the optimal continuation region.
69 For example, like Theorem 1, the proof below exploits existence and uniqueness of solutions to particular BVP,
as well as their continuity with respect to initial conditions.
70 The pure stopping problem that we consider is equivalent to reducing the control set K of Section 2 to a singleton.
71 Continuity of v can be shown by first principles. It follows from the Feller continuity of the diffusion process andcontinuity of terminal payoff function. An elementary argument in the absence of flow payoffs can be found in Peškir
and Shiryaev (2006, p. 154).

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w  (x) + 1

122 σ (x)(−rw(x) + f (x) + μ(x)w  (x)) = 0(28)

with boundary conditions w(x1 ) = v(x1 ) and w(x2 ) = v(x2 ). A standard verification argument
then shows that v coincides with w on any such interval and, therefore, that v is C 2 on suchinterval and, hence, on X \ Y = ∪i∈I Zi , showing iii).
The key is to show that ii) holds at any boundary point of Y, which is the smooth-pasting
property. Consider now the boundary of some interval Zi , for example the upper boundary, andcall it x ∗ . By construction, v(x) > g(x) for x in a left-neighborhood of x ∗ , and v(x ∗ ) = g(x ∗ ).
In particular, vl (x ∗ ), the left derivative of v at x ∗ , must be less than or equal to g  (x ∗ ).
To show that the inequality is tight, suppose, by contradiction, that vl (x ∗ ) < g  (x ∗ ), and consider the domain [x1 , x2∗ = x ∗ + ε] for some x1 in Zi and some small ε > 0. From Appendix B,
there exists a solution w to Eq. (28) on [x1 , x2∗ ], with initial value w(x1 ) = v(x1 ) and initial slopew (x1 ) = v  (x1 ). Moreover, this solution satisfies w(x ∗ ) = v(x ∗ ), and w  (x ∗ ) = vl (x ∗ ), becausev solves the same initial value problem (IVP) on the domain [x1, x ∗ ], and the solution is unique(see Lemma 4). Therefore, w(x) < g(x) for x in a right neighborhood of x ∗ , and without loss, on(x ∗ , x2∗ ). Taking a slightly higher slope ŝ > s ∗ , consider the solution ŵ to the IVP on the domain[x1 , x2∗ ] with initial slope ŝ and initial value v(x1 ). For ŝ close to s, this solution hits g at somex̂ ∈ (x1 , x2∗ ), because solutions to the IVP are continuous in s (see Lemma 5 in Appendix B).
Moreover Lemma 11, also proved in Appendix F, implies that ŵ(x) > w(x) for all x ∈ (x1 , x ∗ ]and, therefore, that x̂ > x ∗ . We redefine ŵ by setting ŵ(x) = g(x) for all x > x̂. By construction, ŵ(x ∗ ) > g(x ∗ ) = v(x ∗ ). Moreover, ŵ corresponds to the expected payoff obtained if thefollowing stopping strategy is used: starting from x ∗ , continue until either x1 or x̂ is reached. Ifx̂ is reached first, stop. If x1 is reached first, follow the initial strategy leading to value v(x1 ).
This strategy thus gives, starting from x ∗ , a strictly higher expected payoff than v(x ∗ ), yielding
a contradiction.
This shows that v is differentiable at x in the following cases: i) x lies in the interior of someinterval Zi , ii) x connects two intervals Zi and Zj (i.e., it is the upper bound of one interval,
and the lower bound of the other), and iii) x is a bound of some interval Zi , and v(y) = g(y) insome neighborhood on the other side of x. Moreover, in cases ii) and iii) the derivative is given
by v  (x) = g  (x). In all these cases, the derivative is continuous at x because the solution to (28)
on any Zi is twice differentiable and because g is C 1 .
There remains to show the result when x is an accumulation point of stopping and continuation
regions. This is proved in Appendix F.1. 2
The next result is a standard verification argument, which directly follows from a simple adaptation of Lemmas 2 and 3. The proof is significantly simpler because there is no control, and thehitting time κ is now defined as the first time that Xt hits Y. We state it here for completeness.72
Proposition 7. Suppose that in addition to Assumptions 2–4, g is twice continuously differentiable except on a set of zero measure and w : X → R has the following properties:
1. w is continuously differentiable, and twice continuously differentiable except on a set of zeromeasure.73
72 A proof can also be found in Krylov (1980), p. 41f.
73 Those properties are enough to apply Itô’s lemma.

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2. w(x) ≥ g(x) for all x ∈ X .
3. For all x ∈ X such that w is twice differentiable,1
rw(x) ≥ f (x) + μ(x)w  (x) + σ 2 (x)w  (x),2with a tight inequality whenever w(x) > g(x)(29)

Then, w is the value function of the optimal stopping problem (27).
The assumption that g is smooth even on the optimal stopping region cannot be easily dispensed with. Décamps et al. (2006) provide an example in which the value function of a candidate
solution is twice differentiable on the continuation region and satisfies smooth pasting, but thesolution is suboptimal since g has a point of non-differentiability on the stopping region.74
5.1. Example: option value
A simple application of the smooth pasting property is to prove, under great generality, thewell-known principle that when the value function of any stopping problem is convex, the agent
waits beyond the myopic optimum to stop, due to an “option value of waiting.” This principle
arises in investment decisions (Dixit, 1989), experimentation problems,75 the exercise of American and real options, and bankruptcy decisions (Leland, 1994).
Formally, suppose that the terminal value function is a constant: g(x) = ḡ for all x, and suppose that it can be established by some means, that the value function v of the agent is convex.76
Consider any maximal interval Z over which continuing is strictly optimal, as in the proof of Theorem 4, with endpoints x and x̄.77 Theorem 4 implies that for any finite endpoint of Z, say x̄,¯
the smooth pasting condition
holds, i.e., v  (x̄) = g  (x̄) = 0. Moreover, the Bellman equation on
Z implies that10 = −rv(x̄) + f (x̄) + μ(x)v  (x̄) + σ 2 (x)vl (x̄),
2where vl (x̄) is the left second derivative of v at x̄, which was shown earlier to always exist under
the assumptions of Theorem 4. Convexity of v and smooth pasting then imply thatf (x̄) ≤ r ḡ,(30)with a strict inequality if vl (x̄) > 0. This shows that the option value of waiting is positive. To
see this, suppose first that ḡ = 0 (stopping yields a zero lump sum). From (30), the agent stops

74 Precisely, Décamps et al. correct and extend a result by Dixit (1993b) in which the optimal stopping payoff is the
maximum of the values of executing two projects, which is non-differentiable at the point of indifference. We are gratefulto an anonymous referee for pointing us to this reference.
75 In experimentation problems with a risky and a safe arm, moving to the safe arm amounts to a stopping problem. Seee.g., Bolton and Harris (1999).
76 Typical arguments include direct reasoning on the strategy of the agents, or reasoning based on the dynamic equationfor v. One version of the former approach is similar to the argument given in Footnote 58, but reversing it: pick any
stopping policy that is optimal for x, and show that the convex combination of the value starting from x1 < x2 , following
x, dominates v(x). This will work if the process Xt is linear in the initial condition x (as is the case for geometric
Brownian motion), and if the flow and terminal payoffs are convex in their argument.
77 As shown in that proof, the strict continuation domain always consists of disjoint open intervals.

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when his current flow payoff is negative. If ḡ is nonzero, the same idea applies, where r ḡ is theflow-payoff equivalent of stopping. The result holds for any shape of the stopping region.
6. Conclusion
This paper provides unifying and self-contained arguments showing, under simple conditions,the smoothness of value functions and the existence of optimal strategies. These arguments
can be extended to analyze situations in which the volatility vanishes over some subset of thestate space, the control domain depends on the state, or control and stopping problems are
combined. They can also be used to derive specific results in applications, such as bounding
the number of switching points in finite multi-armed bandit problems, proving the uniqueness of an optimal threshold in optimal stopping problems, establishing effort monotonicity
in a principal-agent model, or differentiability of the optimal strategy in growth or investment
models.
The analysis has emphasized the role played by comparative statics for dynamic optimization
problems. Envelope theorems were helpful in applications to prove the differentiability of policy
functions and the existence of an optimal control. This suggests that these tools, whose use
is currently largely circumscribed to economic analysis, have a role to play in control theory,
granting them a new, indirect role, for economics.
The assumption of a one-dimensional state space, while accounting for many economic models, is clearly restrictive. For example, we do not consider the case of a state that includes a time
dimension or, in multi-armed bandit problems, the case in which each arm has a separate, independent state. Such extensions are special, as only one state evolves stochastically at any given
time, which may be helpful in obtaining useful generalizations of the methods. More generally,
however, there is a real need in economics for a better understanding of the properties of optimal
policies and value functions with a multidimensional state space, both for qualitative analysis
and for constructing explicit solutions.
Acknowledgments
We are grateful for comments from John Quah and Yuliy Sannikov. Part of this research was
accomplished while the first author was visiting the Economic Theory Center at Princeton University, whose hospitality is gratefully acknowledged. This author is also thankful for financial
support from the NSF under Grant No. 1151410.
Appendix A. Proof of Lemma 1
Consider the function ϕ defined on R by ϕ(x) = |x| for |x| ≥ 1 and ϕ(x) = 12 (1 + x 2 ) for|x| < 1. As is easily checked, ϕ is C 1 everywhere, C 2 except at −1 and 1, and satisfies |ϕ  (x)| ≤ 1
and |x| ≤ ϕ(x) ≤ |x| + 1 for all x ∈ R and |ϕ  (x)| ≤ 1 for x < 1 and ϕ  (x) = 0 for |x| > 1. By
Itô’s lemma,78 we have for any admissible control A, and t ≥ 0
78 Itô’s lemma applies to any function that is C 1 and a.e. C 2 .

B. Strulovici, M. Szydlowski / Journal of Economic Theory 159 (2015) 1016–1055

t
ϕ(XtA ) = ϕ(x) +

1043



1
μ(XsA , As )ϕ  (XsA ) + σ 2 (XsA , As )ϕ  (XsA )
2

ds

0

t
+

σ (XsA , As )ϕ  (XsA )dBs .

0

Because |σ (x, a)| ≤ K σ (1 + |x|), the integrand of the stochastic integral is square integrable
over [0, t].79 Therefore, the stochastic integral has zero mean. Taking expectations, and using thebounds on μ, σ and ϕ we get, letting t = Eϕ(XtA ),tt ≤0 (x) +μμ(K1 + K2


1
)
+
K̃
ds,
s
2

0

where K̃ = max(x,a)∈[−1,1]]×K σ 2 (x, a). By Gröwnwall’s lemma, this implies thatt ≤(μ0 (x) + KX t) exp(K2 t),μwhere KX = K1 + K̃/2. Since |XtA | ≤ ϕ(XtA ) for all t and A, we conclude that E|XtA | ≤μ(|x| + 1 + KX t) exp(K2 t) for all t . In particular, E|XtA |e−rt goes to zero as t goes to infinity. The last claim of the lemma is straightforward to prove, using the linear growth condition
on f .
Appendix B. Proof of Proposition 1
B.1. General results on initial value problems
We start with two results pertaining to the existence of solutions to initial value problems(IVP) and their continuity with respect to the initial conditions. We start with some functionH̃ : (x, y) → H̃ (x, y) defined on X × Rn and taking values in Rn , which satisfies the following
condition:
Condition 4. On any compact interval I of X ,i) |H̃ (x, y)| ≤ M(1 + |y|),ii) |H̃ (x, y) − H̃ (x, y  )| ≤ K|y − y  |,iii) H̃ is continuous in x for each y.
Lemma 4. If Condition 4 holds, the ordinary differential equationy  (x) = H̃ (x, y(x))(31)

with initial condition y(x0 ) = y0 has a unique continuously differentiable solution on X , for any
x0 ∈ X and y0 ∈ Rn .
79 Precisely, square integrability follows from the inequality σ 2 (X A , A ) ≤ 2(K σ )2 (1 + x 2 ), as well as a standardssestimate on E|XtA |2 . See Krylov (1980), Corollary 6, p. 81.

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Let y (x, y0 ) denote the solution to (31) on X with initial condition y (x0 ) = y0 .
Lemma 5. Given Condition 4, y(·, y0 ) is uniformly continuous in y0 .
The proofs are standard and omitted. For Lemma 4 see Hartman (2002, Theorem 1.1, p. 8).80
For Lemma 5, see Hartman (2002, Theorem 2.1, p. 94).
B.2. Proof of Proposition 1: bounded caseWe specialize the results of Section B.1 to our setting: suppose that y = (p, q) and H (x, p, q)satisfies Conditions 1–3. In this case, the function H̃ (x, (p, q)) = (q, H (x, p, q)) satisfies Condition 4.
The proof of Proposition 1 is based on the “shooting method” (see, e.g., Bailey, 1968). The
general intuition for the argument is as follows. We start from some initial conditions (x, υ) and¯ ¯
consider the solution w to the IVP(32)w  + H x, w, w  = 0 
subject to the initial conditions w x = υ and w  x = s. Given our assumptions on H ,¯ a unique, twice continuously differentiable soluLemma 4 guarantees that this IVP will havetion. Lemma 5 guarantees that the solution continuously depends on the starting slope s. We canestablish the existence of a solution to the boundary value problem (BVP) if we can show that it
is always possible to pick the slope s in such a way that at x̄, the solution to the IVP will hit ῡ.
The proof relies on constructing a particular compact, convex subset of (x, υ)-plane, ending
with a vertical segment at x = x̄ that contains ῡ. We then define a mapping between the initialslope s of the solution to the IVP with initial value w(x) = υ and initial slope s, and the “last”¯ is onto. That property then proves¯
point at which it hits the boundary, and show that the mapping
the existence of an initial slope such that the solution hits the value ῡ at x̄.
Lemma 6. There exist positive constants K1 , K2 such that the functions b1 (x) = −K1 − K2 |x|
and b2 (x) = K1 + K2 |x| satisfy the inequalitiesb1 + H x, b1 , b1 > 0b2 + H x, b2 , b2 < 0
 
  
for all x = 0, and the boundary constraints υ ∈ b1 x , b2 x and ῡ ∈ (b1 (x̄) , b2 (x̄)).
Proof. We have for x = 0 and K1 , K2 satisfying Condition 3,b2 (x) + H (x, b2 (x), b2 (x)) = H (x, K1 + K2 |x|), K2 sgn(x)),
which is strictly negative. The argument for b1 is analogous. The boundary constraints are clearlysatisfied provided that K1 and K2 are large enough, and Condition 3 guarantees that we canchoose them that way. 2
80 That theorem establishes local existence. The growth condition, i), guarantees that the solution can be extended tothe entire domain I.

B. Strulovici, M. Szydlowski / Journal of Economic Theory 159 (2015) 1016–1055

1045

Lemma 7. There exist s1 and s2 such that the solution to IVP (32) hits b2 for all initial slopess ≥ s2 and b1 for all initial slopes s ≤ s1 .
Proof. By suitably translating the problem, we can without loss assume that x = υ = 0.81 We¯ ¯ the auxilwish to show that for high enough initial slopes s, the solution ws hits b2 . Consider
iary IVPu + Ku + H (x, u(x), 0) + ε = 0
subject to u(0) = 0 and u (0) = s, where K is the Lipschitz constant of H and ε is a positive
constant. We will show that, for s high enough, u is strictly increasing on [0, x̄], with a derivativethat is bounded below by a linear function of s. For fixed s, let x̃ > 0 denote the first time thatu (x) = 0. On [0, x̃], we have u(x) ≥ 0. By Condition 2, we have H (x, u(x), 0) ≤ H (x, 0, 0) on
that domain, andu (x) + Ku (x) + M ≥ 0,
where M = maxx∈[0,x̄] |H (x, 0, 0)| + ε > 0. Applying Grönwall’s inequality to the function
g(x) = −u (x) − M/K, which satisfies the inequality g  (x) ≤ −Kg(x) on [0, x̃], we concludethatu (x) ≥ [s + M/K] exp(−Kx) − M/K(33)

on that domain. This implies that x̃ is bounded below by1
s + M/K
log,
K
M/K
which exceeds x̄ for s high enough. Moreover, the lower bound on u also implies that u hits b2
for s large enough and does not cross it again before x̄ is reached.
To conclude the proof, we will show that the IVP solution w is above u for any fixed s. The
Lipschitz property of H in its last argument implies that, for all x, u, u ,−Ku ≤ H (x, u, 0) − H x, u, u .
From the definition of u, this implies thatu (x) + H x, u(x), u (x) ≤ −ε < 0
for all x. This implies that w, the solution to the IVP, lies above u, by the following argument. At
x = 0, u and w have the same starting values and slopes, but u has a lower second derivative, by atleast ε, which implies that u < w  in a right neighborhood of 0. We will show that u < w  for all
x in (0, x̄] and, therefore, that u < w on that domain. Suppose by contradiction that there exists
an x > 0 such that u (x) = w  (x), and let x̃ be the first such point. Necessarily, u(x̃) < w(x̃).
Moreover, we haveu (x̃) < −H (x̃, u(x̃), u (x̃)) ≤ −H (x̃, w(x̃), w  (x̃)) = w  (x̃),
where the second inequality is guaranteed by Condition 2. This contradicts the fact that u crossesw  from below at x̃. 2
81 The translation is obtained by letting w̄(x) = w(x − x) − υ and H̄ (x, w, w  ) = H (x − x, w + υ, w  ). H̄ inherits the¯¯¯¯Lipschitz and monotonicity properties of H , as is easily checked.

1046

B. Strulovici, M. Szydlowski / Journal of Economic Theory 159 (2015) 1016–1055

We can finally prove Proposition 1. Let
B = {(x, υ)|b1 (x) = υ or b2 (x) = υ} ∪ [(x̄, b1 (x̄)), (x̄, b2 (x̄))] ⊂ R2 .
B consists of the graph of the functions b1 and b2 on X , along with the vertical segment joining
the endpoints of these graphs at x̄. We also define the function H : [s1 , s2 ] → R2 as the last
hitting point of B for the solution of the IVP with slope s. This function is clearly well defined:
if a solution does not cross b1 or b2 before x̄, it has to hit the vertical segment joining b1 (x̄) andb2 (x̄). From Lemma 7, H(s) is on the graph of b2 for s large and on the graph of b1 for s small(for example, (33) shows that, for s large enough, u cannot cross b2 again after hitting it once).
Moreover, H cannot jump from the graph of b2 to the graph of b1 as s changes, because Lemma 6
implies, for example, that if w crosses b2 , it stays above b2 for all x beyond the crossing point,82
and hence cannot hit b1 . Therefore, H must connect the upper and lower bounds of B as s goes
down. Finally, Lemma 5 implies that H is continuous at any point s for which H(s) lies on thevertical segment. This shows that H(s) must take all values on that segment as it connects thegraphs of b2 and b1 . Since (x̄, ῡ) belongs to that segment, this proves existence of a solution thatsolves the BVP.
B.3. Proof of Proposition 1: unbounded domain
We now prove Proposition 1 when X is unbounded, so that x = −∞ and/or x̄ = +∞. Precisely, we establish the existence of a function v which satisfies ¯w  = −H x, w, w (34)

and |w(x)| ≤ Kv (1 + |x|) on X , where Kv is a positive constant. The arguments of this section
are based on Schrader (1969). For expositional simplicity we focus on the case in which X = R.
The case in which either x or x̄ is finite follows easily from that argument.
¯ we fix a pair K , K of constants that satisfy Condition 3, and let
Throughout the argument
12
b1 , b2 denote the bounds constructed from these constants in Lemma 6. From
B.2, we Appendix
know that the BVP will have a unique C 2 solution for any finite interval χ, χ̄ and boundary 
conditions w χ = υ and w (χ̄) = ῡ that are contained between b1 and b2 . Furthermore, we¯know that the solution satisfies −Kv (1 + |x|) ≤ w (x) ≤ Kv (1 + |x|) on χ , χ̄ , where Kv =max{K1 , K2 } does not depend on the particular interval chosen.

We define a sequence of boundary  value problems such that Eq. (34) holds on x n , x̄nand with boundary conditions w x n = υ n and w (x̄n ) = ῡn for some values υ n , ῡn in(b1 (xn ), b2 (xn )) and (b1 (x̄n ), b2 (x̄n )), respectively, and let x n and x̄n tend to −∞ or +∞, re¯¯
spectively.
Let wn denote the solution to the nth BVP. In the following, we use the Arzelà–Ascoli theorem and show that this procedure indeed yields a solution. For this, we need to prove that thederivatives of wn are equicontinuous, using the following lemma (Hartman, 2002, p. 428).
Lemma 8. Let φ be a nonnegative, continuous function on R+ such that82 The proof of this result is similar to the proof that w stays above u in Lemma 7, showing that w  ≥ b after the2crossing point, and exploits the inequality b2 + H (x, b2 , b2 ) < 0.

B. Strulovici, M. Szydlowski / Journal of Economic Theory 159 (2015) 1016–1055∞sds = ∞,
φ (s)

1047

(35)

0

and let R, x̃ denote two strictly positive constants. Then, there exists a number M such that
if w(x) is C 2 on [0, x̄] with x̄ > x̃ and satisfies |w(x)| ≤ R and w  (x) ≤ φ( w  (x) ), thenw  (x) ≤ M on [0, x̄]. The constant M depends only on R, φ and x̃.

For any bounded domain X0 = χ, χ̄ and any solution w to the BVP on that domain withend values between b1 and b2 , we have|w  (x)| = |H (x, w(x), w  (x)| ≤ |H (x, w(x), 0)| + K|w  (x)| ≤ K̄ + K|w  (x)|(36)

where K is the Lipschitz constant of H over X0 and where the constant K̄ comes from theboundedness of w (which is contained between b1 and b2 ) and continuity of H (·, ·, 0) on thecompact domain X0 .
Since φ (x) = K̄ + Kx satisfies (35) and w is bounded by the functions b1 and b2 , Lemma 8
implies that each wn is bounded on the compact domain X0 , and that the bound is uniform
over n.83 Moreover, (36) implies that the second derivatives of wn are also uniformly boundedon X0 .


We now use the following diagonalization procedure. Consider
a finite domain x 1 , x̄1 . Wehave seen that the functions wn , wn and wn are bounded on x 1 , x̄1 , uniformly in n. By Arzelà–
1
Ascoli’s theorem, 84 there exists a subsequence such that wn converges uniformly to a C functionw̃1 on x 1 , x̄1 . Moreover, the second derivatives {wn }n∈N are also equicontinuous, because
they satisfy wn (x) = −H (x, wn (x), wn (x)) with H continuous and wn and wn equicontinuous.
This implies that there is a subsequence of wn that converges uniformly
to a C 2 function
w̃1



 (x) . By conon [x, x̄]. This also implies that the limit
satisfies
w̃
=
−H
x,
w̃
,
w̃
(x)
(x)
1
1
1


¯
struction,b1 (x) ≤ wn (x) ≤ b2 (x) on x 1 , x̄1 and, therefore, w̃1 is also contained between b1
and b2 .
 To conclude, take the finite domain x 2 , x̄2 ⊃ x 1 , x̄1 . Iterating the last argument,85 there
exists a subsequenceof the first subsequence for which wn converges
uniformly
to a limitfunction w̃2 on x 2 , x̄2 . The functions w̃1 and w̃2 are equal on x 1 , x̄1 . Proceeding iteratively,
 cover the entire domain X . The function w defined by w(x) = w̃k (x) for we canx ∈ x k , x̄k \ x k−1 , x̄k−1 , solves the BVP and is bounded by b1 and b2 . It thus also satisfies|w(x)| ≤ Kv (1 + |x|).
Appendix C. Extension of Berge’s Maximum Theorem to non-compact domains
Proposition 8. Suppose that h : X × K → R is uniformly continuous and X and K are metricspaces. Then, the function H : X → R defined by
83 More precisely, we can apply the Lemma to the function w̄(x) = w(x − χ), so as to have the 0 origin that is assumed¯
in the Lemma.
84 More precisely, we use the following version: any sequence of C 1 functions that have equicontinuous and uniformly
bounded derivatives, and are uniformly bounded at one point, has a subsequence that converges uniformly to a C 1
function. Here, equicontinuity of the derivatives is guaranteed by the uniform bound on the second derivative.

85 Note that the bounds for the domains x , x̄ and x , x̄ are different. However, since we are fixing the domain,
2 2
1 1
we are still able to obtain a convergent subsequence.

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B. Strulovici, M. Szydlowski / Journal of Economic Theory 159 (2015) 1016–1055

H (x) = sup h(x, a)a∈K

is continuous.
Proof. Aliprantis and Border (2006), Lemma 17.29, p. 569, shows that H is lower semicontinuous. It remains to prove that it is upper semicontinuous. For α ∈ R, consider the strict lower levelset Xα = {x : H (x) < α}. We need to prove that Xα is an open set for any α. By construction,
for any x0 ∈ Xα , h (x0 , a) < α for all a. By uniform continuity one may choose, for any ε > 0, a
δ > 0 small enough that for any x  with x  − x0 < δ, we have
sup h x  , a − sup h (x0 , a) ≤ sup h x  , a − h (x0 , a) < ε,

α∈K

α∈K

a∈K

where the first inequality is shown similarly to Footnote 27.86 Therefore, x  ∈ Xα for δ sufficiently small and the set Xα is open. 2
Appendix D. Optimal growth extension: vanishing volatility
This appendix reconsiders the growth example of Section 3.1 and explicitly allows the volatility to reach zero at the origin, illustrating how the techniques used in Appendix B.1 can beextended for this case.
The decision maker solves⎡ τ⎤v(x0 ) = sup E ⎣ e−rt f (XtA , At )dt ⎦A∈A0

subject todXtA = μ(XtA , At )dt + σ (XtA , At )dBt
and X0A = x0 > 0. The state space is given by X = [0, ∞) and the state XtA = 0 is absorbing, sothat XsA = 0 for all s > τ , where τ = inf t ≥ 0 : XtA = 0 . We normalize maxa∈K(0) f (0, a) to 0
and relax Assumptions 1–3, so that the diffusion coefficient σ (x, a) can reach zero at x = 0 andf , μ, and σ do not have to satisfy the Lipschitz assumptions at x = 0.
Assumption 1’. At ∈ K(XtA ) for all t , where K(x) = [0, K(x))] for some nonnegative continuous function K(x).
Assumption 2’. For any [x, x̄] with x > 0, there exists a constant K > 0 such that for all(x, x  , a) satisfying x, x  ∈ [x, x̄] and a ∈ K(x) ∩ K(x  ),






μ (x, a) − μ x  , a + σ (x, a) − σ x  , a + f (x, a) − f x  , a ≤ K|x − x  |,and the functions μ, σ , and f are jointly continuous in (x, a).
86 There, the inequality was shown when the supremum was reached. It is straightforward to extend the proof when it
is not.

B. Strulovici, M. Szydlowski / Journal of Economic Theory 159 (2015) 1016–1055
μ

μ

1049

μ

Assumption 3’. There exist constants K1 , K2 , K σ , and K f such that K2 < r, |μ(x, a)| ≤μμK1 + K2 |x|, |σ (x, a)| ≤ K σ (1 + |x|), and |f (x, a)| ≤ K f (1 + |x|) for all (x, a) such thatx ∈ X and a ∈ K(x). Further, for all x > 0, there exists a constant σ x > 0 such that for allx ∈ [x, ∞), mina∈K(x) σ (x, a) ≥ σ x .
Proposition 9. Let σ0 (x) = mina∈K(x) σ (x, a). Suppose, in addition to Assumptions 1’–3’,
σ0−2 (x) ∈ LP ([0, x̄]) for some P > 1 and x̄ > 0.87 Then, the HJB equation
1
rv(x) = max f (x, a) + v  (x)μ(x, a) + σ 2 (x, a)v  (x)2a∈K(x)
with the boundary condition v(0) = 0 has a unique solution which is twice differentiable on(0, ∞) and continuous at x = 0. This solution equals the decision maker’s value function
on [0, ∞).
The proof is a modification of the argument in Appendix B.3 and relies on the following
extension of Nagumo’s condition, due to Kiguradze (1967).
Lemma 9. Let E = [a, b] × [−r, r] × R for some a < b and r > 0 and let P , Q ∈ [1, ∞] be such1
= 1. If for some continuous functions H : E → R and φ : R+ → R++ and somethat P1 + Q
function ψ : [a, b] → R+ with ψ ∈ LP ([a, b]) we have
∞

1

sQ
ds = ∞φ(s)0and|H (x, p, q)| ≤ ψ(x)φ(|q|),then there exists a number M > 0 such that for any solution tow  (x) = H (x, w(x), w  (x))
on [a, b] with |w(x)| ≤ r, we have|w  (x)| < M.

We again consider a sequence of domains x n , x̄n with x n → 0 and x̄n → ∞ as n → ∞ andthe associated boundary value problems with boundary conditions w(x n ) = v n and w(
 x̄n ) = v̄nand solutions wn (x). We choose v n → 0 and v̄n as in Appendix B.3. On any domain x, x̄ with
0 < x < x̄ < ∞,H (x, p, q) = max

1a∈K(x) σ 2 (x, a)(−rp + f (x, a) + μ(x, a)q)

satisfies Conditions 1–3 used in the proof of Proposition 2 and a subsequence of wn (x) converges
to the twice differentiable function w(x).
87 For any interval I of R, φ ∈ LP (I ) if and only if (to the Lebesgue measure.

1

P
P
I (|φ(x)|) dx) < ∞, where the integral is taken with respect

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B. Strulovici, M. Szydlowski / Journal of Economic Theory 159 (2015) 1016–1055

It remains to show that the function w(x) constructed by this method is continuous at zero.
Since
1|H (x, p, q)| ≤max (r|p| + |f (x, a)| + |μ(x, a)||q|)σ02 (x) a∈K(x)1≤

σ02 (x)(K̄ + K|q|),taking ψ(x) =1and φ(x) = K̄ + Ks in Lemma 9 implies that on any finite interval [0, x̄],σ02 (x)wn (x) is bounded whenever wn (x) is bounded.88 On any such [0, x̄], the sequence wn (x) is thus

equicontinuous and a subsequence converges uniformly to w(x) by the Arzelà–Ascoli theorem.
Therefore, w(x) is continuous at zero. Since w(x) is twice differentiable except at zero, theverification argument in Lemmas 3 and 4 applies without modification.
Example 1. Take a version of the neoclassical growth model in which the capital stock evolvesasρ

dkt = (ktα − δkt − ct )dt + σ kt dBt
with initial condition k0 > 0 and 0 < ρ < 12 . Here, δ > 0 is the depreciation rate and k α with α ∈(0, 1) is the production function. With CRRA utility u(c) = c 1−γ−1 for γ ∈ (0, 1) and feasible
consumption set K(k) = [0, Mk] for M > 0 chosen sufficiently large, this problem satisfies theassumptions of Proposition 9.
(1−γ )

Appendix E. Results for the multi-armed bandit application
Lemma 10. The value function v is convex in the belief x.
Proof. The argument is standard and replicated for completeness. For any admissible control
process A, we have⎡∞⎤
v(x, A) = E ⎣ e−rt π(At , θ̃ )dt|x ⎦0

By definition, any admissible process A is adapted to the observation filtration, which, conditional on the true state θ̃ does not depend on the probability x. Therefore, we have
⎡∞
⎤
⎡∞
⎤


v(x, A) = xE ⎣ e−rt π(At , θH )dt|θH ⎦ + (1 − x)E ⎣ e−rt π(At , θL )dt|θL ⎦ ,00which shows that v(x, A) is linear in x. For x1 < x2 and λ ∈ (0, 1), let x = λx1 + (1 − λx2 ). We
have, letting A∗ denote an optimal process given the initial belief x,89
1

88 Note that∞ sq
0 K̄+Ks ds = ∞ for any 1 ≤ q < ∞.

89 We do not need to assume the existence of an optimum: the argument is easily adapted by taking a sequence ofcontrols An such that v(x, An ) converges to v(x) as n → ∞.

B. Strulovici, M. Szydlowski / Journal of Economic Theory 159 (2015) 1016–1055

1051

v(x) = v(x, A∗ ) = λv(x1 , A∗ ) + (1 − λ)v(x2 , A∗ ) ≤ λv(x1 ) + (1 − λ)v(x2 ),where the second equality comes from the linearity of v(·, A) in x, and the inequality comesfrom the definition of the v(xi )’s. 2
Proposition 10. With finitely many arms, the are finitely many cutoffs, and the number of cutoffs
is bounded by twice the number of arms.
Proof. From Theorem 1, the value function solves everywhere the HJB equation, which may be
rewritten as


1

2 2
rv(x) = max xf (i) + fL (i) + v (x) (x (1 − x)) ξ (i)
(37)
2
i∈K
L (i)
where f (i) = fH (i) − fL (i). If v  (x) = 0 for some x ∈ (0, 1), then v(x) = xf (i)+fforrsome i, which means that playing i forever is optimal and there is no cutoff. Thus, we focus on
the case v  (x) > 0 for all x ∈ (0, 1).
Rewriting the HJB equation, we havev  (x) = mini∈Krv(x) − xf (i) − fL (i)1
2 2
2 (x (1 − x)) ξ (i).

Moreover, lettingwi (x) =rv (x) − xf (i) − fL (i),ξ 2 (i)

Arm i maximizes (37) if and only if wi (x) ≤ wj (x) for all j in K.
We haveξ 2 (j ) − ξ 2 (i)wi − wj (x) = rv  (x).
ξ 2 (i)ξ 2 (j )Lemma 10 therefore implies that either wi − wj or wj − wi is convex. If wi − wj is linear, either
the arms are identical, in which case they can be treated as a single arm, or wi − wj crosses
zero at most once. Otherwise, wi − wj is strictly convex or strictly concave, since v  is strictly
positive, and crosses zero at most twice. Since the number of arms is finite, this implies thatthe set Zi = {x : wi (x) > wj (x) ∀j = i} consists of finitely many open intervals, and that theset Z of x ∈ [0, 1] such that arg maxi {wi (x)} is not a singleton is finite. This shows that any
selection of maximizers of the HJB equation has finitely many switches, yielding a volatility thathas bounded variation. Theorem 2 then implies that such selection is optimal. It also implies thatthe (essentially unique) optimal strategy has finitely many cutoffs. Because any two arms canonly change dominance twice, this also shows that the number of switching points is bounded bytwice the number of arms: if an arm is strictly optimal in two disjoint regions, then any arm thatis strictly optimal in a region contained between these two regions can only be optimal withinthese two regions. The result then follows by an easy induction argument. 2
Proposition 11. A given arm may be used over disjoint intervals of beliefs.
Proof. Consider a four-armed setting with the following characteristics. Arms 1 and 2 havesymmetric payoffs around the belief 1/2: f1 (x) = f2 (1 − x), with f1 (0) > f1 (1), and have the1052

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same low signal-to-noise ratio ξ(1) = ξ(2) = ε > 0. The payoffs f3 and f4 are independent90
of x, with f1 (0) > f3 > f4 and min{ξ(3), ξ(4)} > ε.
Notice that, for extreme beliefs, either Arm 1 or 2 is used. Moreover, because Arm 1 is optimalfor very low beliefs whereas Arm 2 is optimal for very high ones, the value of information isstrictly positive in this problem.
It suffices to show that Arms 3 and 4 are both chosen on a positive domain. By symmetry,
this will imply that one arm is chosen on both sides (the one that is not used at x = 1/2).91
Consider, first, the case in which only Arms 1, 2, and 3 are available. Because Arms 1 and 2
have low signal to noise ratio, using Arm 3 is optimal for intermediate beliefs, provided that f3
is high enough (but still below f1 (0)). We now add in Arm 4, which has a low payoff. We will
gradually increase its signal to noise ratio, ξ(4). For ξ(4) < ξ(3), Arm 4 is dominated by Arm 3
both in terms of payoff and informativeness, and thus not used at all. For ξ(4) arbitrarily high,using Arm 4 reveals the state of the world almost instantaneously, which is clearly optimal forintermediate beliefs. Therefore, there must exist a threshold ξ4∗ > ξ(3) above which it becomes
strictly optimal to use Arm 4 around some belief x ∗ . At ξ4∗ , v4 − v3 has a local maximum at x ∗ ,which has to be in the interior of [0, 1]. From the proof of Proposition 10, this implies that v4 − v3
is strictly concave and that x ∗ is the unique global maximum. This implies that v3 (x) > v4 (x)for all x = x ∗ and, since it is always optimal to use either Arm 3 or Arm 4 on an interval ofstrictly positive measure, this implies that v3 (x  ) > max{v1 (x  ), v2 (x  ), v4 (x  )} on some intervalZ  of positive measure. By increasing ξ(4) slightly above ξ4∗ , Arm 4 becomes strictly optimalaround x ∗ (by definition of x ∗ ), while Arm 3 remains strictly optimal on Z  , which proves theclaim. 2
Appendix F. Proofs of Section 5 (smooth pasting)Lemma 11. Consider vs̃ and vs , two solutions to the IVP with starting slopes s̃ > s on an interval[x1 , x2 ] which both satisfy vs̃ (x1 ) = vs (x1 ) = v1 . Then, vs̃ (x) > vs (x) for all x ∈ (x1 , x2 ].
 
 


Proof. Let x̂ = inf x : vs̃ (x) ≤ vs (x) . Note that x̂ > x because vs̃ x > vs x and both vs̃ andvs are C 2 . By construction, vs̃ x̂ > vs x̂ . Since both solutions satisfy the equationv  (x) + 1

12 σ (x)2

−rv (x) + f (x) + μ (x) v  (x) = 0

we have vs̃ x̂ =

  
 
   1
rvs̃ x̂ + f x̂ + μ x̂ vs̃ x̂
2
12 σ x̂
1> 1
rvs (x̂) + f (x̂) + μ(x̂)vs (x̂) = vs (x̂)
22 σ (x̂)

Since vs̃ (x) must hit vs (x) from above as x reaches x̂, we obtain a contradiction. 2
90 Thus, for simplicity, Arms 3 and 4 are informative despite having state-independent payoffs. It is easy to slightly
perturb those payoffs to introduce payoff dependence and preserve the qualitative shape of the choice intervals.
91 If x = 1/2 was a cutoff, then both Arms 3 and 4 are used on each side of x = 1/2, which also proves the proposition.

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1053

F.1. End of the proof of Theorem 4
To conclude the proof, we need to show the result when x is such that v(x) = g(x), but x is an
accumulation point of stopping and continuation regions, on either its right side or its left side,or both.
Without loss of generality, we set x = 0 and prove that vr (0) = g  (0), where vr is the rightderivative of v at 0. We wish to show that limη↓0 (v(η) − v(0))/η converges to g  (0). Consider
any η > 0. The difference v(η) − v(0) is either equal to g(η) − g(0), if η belongs to Y, orelse η belongs to some interval Zi close to 0. Let y denote the lower bound of Zi . By twice
differentiability of v on (y, η), and because the right derivative of v at y is equal to g  (y), we
have v(η) = v(y) + g  (y)(η − y) + 12 vr (z1 )(η − y)2 for some z1 ∈ (y, η). Since v(y) = g(y),we have g(η) = v(y) + g  (z2 )(η − y) for some z2 ∈ (y, η). Therefore, v(η) = g(η) + (g  (y) −g  (z2 ))(η − y) + 12 vr (z1 )(η − y)2 , and
v(η) − v(0) g(η) − g(0)
η−y
1 


=
+ 1η∈/ Yg (y) − g (z2 ) + vr (z1 )(η − y) .
ηηη2Taking the limit as η goes to zero yields the result, if we can show that g  (y) − g  (z2 ) +1 
2 vr (z1 )(η − y) converges to zero as η → 0. The first two terms cancel each other in the limit,
as they both converge to g  (0) (since g is C 1 ). The last term converges to 0 if we can show thatv  (·) is uniformly bounded on all the intervals Zi in a neighborhood of 0. This uniform boundis guaranteed by Lemma 8 (Appendix B.3), which guarantees a uniform upper bound on |v  | andon |v  |.92
Continuity of v  is shown by a similar argument. For any ε > 0, there exists η̄(ε) such that|g (η) − g  (0)| ≤ ε/2 for all η ≤ η̄(ε). As was mentioned earlier, v  is uniformly bounded on theinterval [x, x + η̄(ε)] ∩ ∪i∈I Zi , by some constant M. Let η(ε) = min{η̄(ε), ε/M}. Consider anyη < η(ε). If η ∈ Y, then v  (η) = g  (η) and |v  (η) − v  (0)| < ε/2. Otherwise we have, using thevariable y introduced earlier in the proof,v  (η) = v  (y) + v  (z3 )(η − y) = g  (y) + v  (z3 )(η − y),for some z3 ∈ (y, η). This implies that|v  (η) − v  (0)| < |g  (y) − g  (0)| + Mη < ε.
Proceeding similarly to the left of x, we conclude that v  is continuous at x.
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